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.

Impacts of glucagon-like peptide-1 receptor agonists on the risk of adverse liver outcomes in patients with metabolic dysfunction-associated steatotic liver disease cirrhosis and type 2 diabetes.

BACKGROUND/AIMS: We examined the effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) initiation on long-term Adverse Liver Outcomes (ALO) in patients with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) cirrhosis and type 2 diabetes using real-world data from the MarketScan database. METHODS: We conducted a retrospective cohort study of patients with MASLD cirrhosis and type 2 diabetes between 2012 and 2020. Cox proportional hazard models examine the association between GLP-1RAs initiation, modelled as time-dependent, and the risk of ALO, a composite endpoint defined by the first occurrence of hepatic decompensation(s), portal hypertension, hepatocellular carcinoma (HCC) or liver transplantation (LT). We used Overlap Propensity Score Weighting (OPSW) to account for confounding. The study included 459 GLP-1RAs and 4837 non-GLP-1RAs patients. RESULTS: The non-GLP-1RAs patients presented with 1411 (29%) ALO over 7431.7 person years, while GLP-1RAs patients had 32 (7%) ALO over 586.6 person years - risk rate difference 13.5 (95% CI: 11.4-15.7) per 100 person-years. The OPSW-adjusted risk of ALO was reduced by 36% (hazard ratio [HR]: 0.64; 95% CI: 0.54-0.76) in patients with vs. without GLP-1RAs initiation. GLP-1RAs initiation was associated with significant reductions in the adjusted risk of hepatic decompensation (HR: 0.74; 95% CI: 0.61-0.88), portal hypertension (HR: 0.73; 95% CI: 0.60-0.88), HCC (HR: 0.37; 95% CI: 0.20-0.63) and LT (HR: 0.24; 95% CI: 0.12-0.43). CONCLUSION: The use of GLP-1RAs was associated with significant risk reductions in long-term adverse liver outcomes, including hepatic decompensation, portal hypertension, HCC and LT, in MASLD cirrhosis patients with type 2 diabetes.

Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults.

INTRODUCTION: Polypharmacy is common and is associated with higher risk of adverse drug event (ADE) among older adults. Knowledge on the ADE risk level of exposure to different drug combinations is critical for safe polypharmacy practice, while approaches for this type of knowledge discovery are limited. The objective of this study was to apply an innovative data mining approach to discover high-risk and alternative low-risk high-order drug combinations (e.g., three- and four-drug combinations). METHODS: A cohort of older adults (≥ 65 years) who visited an emergency department (ED) were identified from Medicare fee-for-service and MarketScan Medicare supplemental data. We used International Classification of Diseases (ICD) codes to identify ADE cases potentially induced by anticoagulants, antidiabetic drugs, and opioids from ED visit records. We assessed drug exposure data during a 30-day window prior to the ED visit dates. We investigated relationships between exposure of drug combinations and ADEs under the case-control setting. We applied the mixture drug-count response model to identify high-order drug combinations associated with an increased risk of ADE. We conducted therapeutic class-based mining to reveal low-risk alternative drug combinations for high-order drug combinations associated with an increased risk of ADE. RESULTS: We investigated frequent high-order drug combinations from 8.4 million ED visit records (5.1 million from Medicare data and 3.3 million from MarketScan data). We identified 5213 high-order drug combinations associated with an increased risk of ADE by controlling the false discovery rate at 0.01. We identified 1904 high-order, high-risk drug combinations had potential low-risk alternative drug combinations, where each high-order, high-risk drug combination and its corresponding low-risk alternative drug combination(s) have similar therapeutic classes. CONCLUSIONS: We demonstrated the application of a data mining technique to discover high-order drug combinations associated with an increased risk of ADE. We identified high-risk, high-order drug combinations often have low-risk alternative drug combinations in similar therapeutic classes.

Promotion of S-nitrosation of cysteine by a {Co(NO)2}10 complex.

S-Nitrosothiols (SNOs) serve as endogenous carriers and donors of NO within living cells, releasing nitrosonium ions (NO+), NO, or other nitroso derivatives. In this study, we present a bioinspired {Co(NO)2}10 complex 1 that achieved S-nitrosation towards Cys residues. The incorporation of a ferrocenyl group in 1 allowed for fine-tuning of the nitrosation reaction, taking advantage of the redox ability of Cys residues. Complex 1 was synthesized and characterized, demonstrating its NO translation reactivity. Furthermore, complex 1 successfully converted Cys into S-nitrosocysteine (Cys-SNO), as confirmed by UV-Vis, IR, and XAS spectroscopy. This study presents a promising approach for S-nitrosation of Cys residues for further exploration in the modification of Cys-containing peptides.

Versatile Synthesis of Symmetric and Unsymmetric Imines via Photoelectrochemical Catalysis: Application to N-Terminal Modification of Phenylalanine.

A strategy that combines electrochemical synthesis and photoredox catalysis was reported for the efficient synthesis of imines. This approach was demonstrated to be highly versatile in producing various types of imines, including symmetric and unsymmetric imines, by exploring the impact of different substituents on the benzene ring of the arylamine. Additionally, the method was specifically applied to modify N-terminal phenylalanine residues and was found to be successful in the photoelectrochemical cross-coupling reaction between NH2 -Phe-OMe and aryl methylamines, leading to the synthesis of phenylalanine-containing imines. Therefore, this technique would present a convenient and efficient platform for synthesizing imines, with promising applications in chemical biology, drug development, and organic synthesis.

Photoredox C-H functionalization leads the site-selective phenylalanine bioconjugation.

Site-selectively chemical bioconjugation of peptides and proteins can improve the therapeutic exploration of modified protein drugs. Only 3.8% natural abundance of phenylalanine in protein and nearly 90% of proteins contain at least one phenylalanine residue in their sequenced, showing the potential in biopharmaceutical utility of the phenylalanine bioconjugation. However, the covalent bioconjugation of native phenylalanine is one of the most challenging problems in protein modification. Herein, an approach to protein modification is described that relies on a photoredox method for the site-selective bioconjugation of phenylalanine. This methodology has been validated on peptides as well as protein insulin using a straightforward and mild condition. In addition, based on characterization by near-UV CD spectroscopy and small angle X-ray scattering (SAXS), this pyrazole labeling approach permitted the insulin hexamer to completely dissociate into the monomeric form, thus making it a potential candidate for use as rapid-acting insulin for the treatment of diabetes.

PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature.

BACKGROUND: Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks. RESULTS: Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the "Biomedical & Procedure" dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for "The use of NLP". The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions. CONCLUSIONS: The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications.

Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. METHODS: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. RESULTS: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3ß) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. CONCLUSIONS: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach.

BACKGROUND: While the pharmacokinetic (PK) mechanisms for many drug interactions (DDIs) have been established, pharmacovigilance studies related to these PK DDIs are limited. Using a large surveillance database, a translational informatics approach can systematically screen adverse drug events (ADEs) for many DDIs with known PK mechanisms. METHODS: We collected a set of substrates and inhibitors related to the cytochrome P450 (CYP) isoforms, as recommended by the United States Food and Drug Administration (FDA) and Drug Interactions Flockhart table™. The FDA's Adverse Events Reporting System (FAERS) was used to obtain ADE reports from 2004 to 2018. The substrate and inhibitor information were used to form PK DDI pairs for each of the CYP isoforms and Medical Dictionary for Regulatory Activities (MedDRA) preferred terms used for ADEs in FAERS. A shrinkage observed-to-expected ratio (Ω) analysis was performed to screen for potential PK DDI and ADE associations. RESULTS: We identified 149 CYP substrates and 62 CYP inhibitors from the FDA and Flockhart tables. Using FAERS data, only those DDI-ADE associations were considered that met the disproportionality threshold of Ω > 0 for a CYP substrate when paired with at least two inhibitors. In total, 590 ADEs were associated with 2085 PK DDI pairs and 38 individual substrates, with ADEs overlapping across different CYP substrates. More importantly, we were able to find clinical and experimental evidence for the paclitaxel-clopidogrel interaction associated with peripheral neuropathy in our study. CONCLUSION: In this study, we utilized a translational informatics approach to discover potentially novel CYP-related substrate-inhibitor and ADE associations using FAERS. Future clinical, population-based and experimental studies are needed to confirm our findings.

A multistate transition model for statin-induced myopathy and statin discontinuation.

The overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.

Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data.

Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug event (ADEs) signals. Existing control selection approaches for case-control design included the dynamic/super control selection approach. The dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrollment history. Thus, using large-scale longitudinal health data, the dynamic/super control selection approach requires extraordinarily high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. The random control selection approach does not depend on ADE/enrollment history. It is able to significantly reduce computational time to prepare case-control data sets, as it requires all individuals to be evaluated only once. We compared the performance metrics of all control selection approaches using two large-scale longitudinal health data and a drug-ADE gold standard including 399 drug-ADE pairs. The F-scores for the random control selection approach were between 0.586 and 0.600 compared to between 0.545 and 0.562 for dynamic/super control selection approaches. The random control selection approach was ~ 1000 times faster than dynamic/super control selection approach on preparing case-control data sets. With large-scale longitudinal health data, a case-control design-based pharmacoinformatics study using random control selection is able to generate comparable ADE signals than the existing control selection approaches. The random control selection approach also significantly reduces computational time to prepare the case-control data sets.

Reducing acetylated tau is neuroprotective in brain injury.

Traumatic brain injury (TBI) is the largest non-genetic, non-aging related risk factor for Alzheimer's disease (AD). We report here that TBI induces tau acetylation (ac-tau) at sites acetylated also in human AD brain. This is mediated by S-nitrosylated-GAPDH, which simultaneously inactivates Sirtuin1 deacetylase and activates p300/CBP acetyltransferase, increasing neuronal ac-tau. Subsequent tau mislocalization causes neurodegeneration and neurobehavioral impairment, and ac-tau accumulates in the blood. Blocking GAPDH S-nitrosylation, inhibiting p300/CBP, or stimulating Sirtuin1 all protect mice from neurodegeneration, neurobehavioral impairment, and blood and brain accumulation of ac-tau after TBI. Ac-tau is thus a therapeutic target and potential blood biomarker of TBI that may represent pathologic convergence between TBI and AD. Increased ac-tau in human AD brain is further augmented in AD patients with history of TBI, and patients receiving the p300/CBP inhibitors salsalate or diflunisal exhibit decreased incidence of AD and clinically diagnosed TBI.

Late-Stage Photoredox C-H Amidation of N-Unprotected Indole Derivatives: Access to N-(Indol-2-yl)amides.

The late-stage functionalization of N-unprotected indoles can be useful for modifying low-molecular-weight drugs and bioactive peptides. Whereas indole carboxamides are valuable in pharmaceutical applications, the preparation N-(indol-2-yl)amides with similar structures continues to be challenging. Herein we report on visible-light-induced late-stage photoredox C-H amidation with N-unprotected indoles and tryptophan-containing peptides, leading to the formation of N-(indol-2-yl)amide derivatives. N-Unprotected indoles and aryloxyamides that contain an electron-withdrawing group could be coupled directly to eosin Y as the photocatalyst by irradiation with a green light-emitting diode at room temperature. Mechanistic studies and density functional theory calculations indicate that the transformation might proceed through the oxidative C-H functionalization of indole with a PS* to PS•- cycle. This protocol provides a new toolkit for the late-stage modification labeling and peptide-drug conjugation of N-unprotected indole derivatives.

Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease.

Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.

Comparative assessment of manual chart review and ICD claims data in evaluating immunotherapy-related adverse events.

BACKGROUND: The aim of this retrospective study was to demonstrate that irAEs, specifically gastrointestinal and pulmonary, examined through International Classification of Disease (ICD) data leads to underrepresentation of true irAEs and overrepresentation of false irAEs, thereby concluding that ICD claims data are a poor approach to electronic health record (EHR) data mining for irAEs in immunotherapy clinical research. METHODS: This retrospective analysis was conducted in 1,063 cancer patients who received ICIs between 2011 and 2017. We identified irAEs by manual review of medical records to determine the incidence of each of our endpoints, namely colitis, hepatitis, pneumonitis, other irAE, or no irAE. We then performed a secondary analysis utilizing ICD claims data alone using a broad range of symptom and disease-specific ICD codes representative of irAEs. RESULTS: 16% (n = 174/1,063) of the total study population was initially found to have either pneumonitis 3% (n = 37), colitis 7% (n = 81) or hepatitis 5% (n = 56) on manual review. Of these patients, 46% (n = 80/174) did not have ICD code evidence in the EHR reflecting their irAE. Of the total patients not found to have any irAEs during manual review, 61% (n = 459/748) of patients had ICD codes suggestive of possible irAE, yet were not identified as having an irAE during manual review. DISCUSSION: Examining gastrointestinal and pulmonary irAEs through the International Classification of Disease (ICD) data leads to underrepresentation of true irAEs and overrepresentation of false irAEs.

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

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

Electrooxidation Enables Selective Dehydrogenative [4+2] Annulation between Indole Derivatives.

Dearomative annulation of indoles has emerged as a powerful tool for the preparation of polycyclic indoline-based alkaloids. Compared with well-established methods towards five-membered-ring-fused indolines, the six-membered-ring-fused indolines are rarely accessed under thermal conditions. Herein, a dearomative [4+2] annulation between different indoles is developed through an electrochemical pathway. This transformation offers a remarkably regio- and stereoselective route to highly functionalized pyrimido[5,4-b]indoles under oxidant- and metal-free conditions. Notably, this electrochemical approach maintains excellent functional-group tolerance and can be extended as a modification tactic for pharmaceutical research. Preliminary mechanism studies indicate that the electrooxidation annulation proceeds through radical-radical cross-coupling between an indole radical cation and an N-centered radical generated in situ.