S. Scott Sutton

Innate immune signaling through inflammasome activation has been implicated in the pathogenesis of multiple sclerosis (MS). We previously demonstrated that nucleoside reverse transcriptase inhibitors (NRTIs), drugs approved to treat HIV and hepatitis B virus infections, and kamuvudines, safer NRTI derivatives, both inhibit inflammasome activation. Here, we report that in the experimental autoimmune encephalitis mouse model of MS, treatment with kamuvudine-9 (K-9), an NRTI derivative with an enhanced safety profile, prevented further neurological deficits and reversed preexisting paralysis and vision loss. K-9 promoted myelin and axonal preservation in the mouse spinal cord and abolished the increase in serum neurofilament light (NfL) chain. K-9 disrupted interactions between nucleotide-binding domain leucine-rich repeat (NLR) pyrin domain-containing protein 3 (NLRP3) and NIMA-related kinase 7 (NEK7) and between NLRP3 and NLR CARD domain containing 4 (NLRC4), inhibiting dual inflammasome activation. K-9 also exhibited appropriate safety and pharmacokinetic characteristics and biodistribution. In three distinct human cohorts, there was a lower incidence of MS in those individuals receiving NRTIs for HIV infection, preexposure prophylaxis for HIV, or hepatitis B virus infection. NRTI use was also associated with a reduction in relapse rate in individuals with MS. These findings provide a rationale for further investigating K-9 for treating MS.

Xanthine oxidase (XO) inhibitors remain the standard pharmacologic therapy for hyperuricemia. However, additional enzymatic components of the purine degradation pathway may contribute to uric acid synthesis. Guanine deaminase (GD) catalyzes the conversion of guanine to xanthine, providing substrate for downstream oxidation by XO. Cyclovir antivirals (acyclovir, valacyclovir, and famciclovir) are guanine nucleoside analogues that function as alternative substrates for GD and may influence urate production through substrate‐level interactions. We conducted retrospective analyses using two independent real‐world data sources: the Merative MarketScan Research Databases with linked laboratory data and the US Veterans Health Administration. Patients exposed to cyclovir antivirals were compared with untreated controls with herpes simplex virus infection. The primary outcome was serum uric acid (sUA) measured approximately 1 year after index. Multivariable linear regression and inverse probability weighting were used to estimate adjusted differences in follow‐up sUA. In the MarketScan cohort, associations were most pronounced among patients with elevated baseline sUA (≥ 6.8 mg/dL), with inverse probability–weighted analyses demonstrating an adjusted difference of −0.87 mg/dL (95% CI: −1.59 to −0.14; p = 0.02). In the Veterans Health Administration cohort, cyclovir treatment was associated with an adjusted difference of −0.87 mg/dL (95% CI: −1.43 to −0.30; p = 0.003). Cyclovir antiviral exposure was associated with modest reductions in serum uric acid in two independent real‐world populations. Further mechanistic and prospective studies are warranted to clarify the role of GD and related pathway components in urate regulation.

Introduction: Factor Xa inhibitors (FXai) are associated with a risk of serious bleeding, including intracranial hemorrhage (ICH), which can lead to long-term disability. Functional status, often measured using the modified Rankin Scale (mRS), is a key outcome after ICH but is rarely recorded in structured electronic medical record (EMR) fields. This study used natural language processing (NLP) in the retrospective derivation of mRS scores from unstructured clinical notes and evaluate clinical correlates of functional outcomes in a national cohort of US veterans with FXai-related ICH. Methods: This retrospective cohort study used data from the Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI) from March 2014 to May 2022. Adults (≥18 years) hospitalized for direct or indirect FXai-related ICH and treated with reversal or replacement agents were included. Functional outcomes were assessed using mRS scores derived via NLP of unstructured clinical notes. Glasgow Coma Scale (GCS) scores were extracted using an algorithm to support model adjustment and NLP reliability. Results: The cohort included 269 patients (mean age 75.7 years; 96.7% male). An NLP algorithm was used to retrospectively derive mRS scores, and GCS scores were extracted using an algorithm to support NLP accuracy and model adjustment. Favorable functional status (mRS 0–3) was observed in 36.1% at baseline, increasing to 58.3% at 90 days. Higher baseline mRS and Charlson Comorbidity Index (CCI) were independently associated with worse functional outcomes (mRS ≥ 4) at discharge and 90 days ( P ≤ 0.039). Discussion: Retrospective derivation of mRS scores from clinical documentation is feasible using NLP. This approach demonstrates functional outcome assessment in large-scale EMR-based studies when direct measurement is unavailable. Because long-term recovery after ICH is often defined by functional status rather than survival alone, incorporating mRS into retrospective analyses may improve the evaluation of interventions for FXai-related ICH.

Background: This study evaluated clinical and economic outcomes associated with antiretroviral therapy (ART) resistance among people with HIV (PWH) within a nationally representative database. Methods: Retrospective claims data were extracted from the Veterans Affairs Informatics and Computing Infrastructure. PWH receiving testing for resistance-associated mutations (RAMs) between 2003 and 2023 were categorized as resistant if they had ≥1 documented RAM in integrase strand transfer inhibitor, protease inhibitor, or reverse transcriptase genes. Results: Among 7746 veterans with interpretable resistance tests, 1875 had no detectable resistance, 4466 had ≥1 major or minor RAM (any RAM), and 1405 had ≥1 major RAM. The median CD4 count was highest for those without RAMs (357 cells/mm3) and lowest for those with major RAMs (285 cells/mm3). Opportunistic infections during the 1-year follow-up were present in 3.8% without RAMs as compared with >5% for both RAM groups. Hospitalizations were more frequent in the major RAM and any RAM cohorts vs the no resistance cohort (P < .001 for both). Total all-cause costs averaged $45 476 for those without RAMs as compared with $49 945 for those with any RAM and $48 392 for those with a major RAM. Among PWH initiating ART after resistance testing (n = 3522), those without RAMs had the longest persistence, while those with RAMs had shorter persistence (P < .0001). PWH prescribed ART at the time of resistance testing who had RAMs were most likely to switch to a regimen based on an integrase strand transfer inhibitor or protease inhibitor. Conclusions: In this real-world analysis, PWH with detectable resistance had lower CD4 counts, more frequent hospitalizations and opportunistic infections, greater economic costs, and increased discontinuation of ART.

To determine whether bisphosphonate use increases the risk of age-related macular degeneration (AMD), thereby providing evidence for the involvement of the NLRP3 inflammasome in AMD pathogenesis. Retrospective cohort study among US veterans who had undergone dual-energy x-ray absorptiometry (DEXA) scans. Time-dependent Cox models were used to assess the association between cumulative bisphosphonate exposure and AMD incidence. Propensity score matching was applied to balance characteristics between bisphosphonate users and nonusers. A secondary analysis examined the impact of NLRP3 inhibitors (fluoxetine and fluvoxamine) on AMD risk among bisphosphonate users. After propensity score matching, each additional year of bisphosphonate use was associated with a 4.7% increased hazard of AMD (hazard ratio [HR], 1.047; 95% confidence interval [CI], 1.020-1.074). In the secondary analysis, fluoxetine or fluvoxamine use among bisphosphonate users was linked to a reduced hazard of incident AMD (HR, 0.814; 95% CI, 0.676-0.98) in the matched sample. Bisphosphonate use increases AMD risk, while NLRP3 inhibitors mitigate this effect. These findings support the hypothesis that the NLRP3 inflammasome is involved in AMD pathogenesis and represents a potential therapeutic target.

Aim: To compare the safety and efficacy of antidepressants (AD) among older adults with major depressive disorder (MDD) by assessing treatment change, augmentation and hospitalization rates. Methods: This retrospective study analyzed data from the Veterans Affairs (VA) database, including 142,138 patients aged ≥60 years diagnosed with MDD. Patients prescribed bupropion, citalopram, duloxetine, escitalopram, fluoxetine, mirtazapine, paroxetine, sertraline, or venlafaxine were included. Outcomes were treatment change, augmentation and hospitalization rates. Hazard ratios (aHRs) were calculated using sertraline as the reference. Results: Of the patients, 39.6% required augmentation, 18.1% changed antidepressant treatment and 13.3% were hospitalized. The corresponding incidence rate was 544, 124 and 122 events per 1000 person-years. Compared with sertraline, mirtazapine users had the highest AD change risk (aHR 1.34, 95% CI: 1.29–1.40), while duloxetine users had the lowest (aHR 0.87, 95% CI: 0.83–0.92). Duloxetine also had the lowest augmentation risk (aHR 0.89, 95% CI: 0.86–0.92). Mirtazapine users also had the highest risks of augmentation (aHR 1.15, 95% CI: 1.12–1.18) and hospitalization (aHR 1.14, 95% CI: 1.07–1.23). Bupropion had the lowest hospitalization risk (aHR 0.77, 95% CI: 0.71–0.84). Conclusion: Antidepressant choice significantly influences treatment outcomes in older adults with MDD. Duloxetine demonstrated the best profile with the lowest risks of AD change and augmentation, while mirtazapine posed the highest risks of all three outcomes. Personalized treatment strategies are crucial to improving outcomes in this population.

Background: Chronic inflammation is a key factor in type 2 diabetes mellitus (T2DM) development. The cholinergic anti‐inflammatory pathway (CAP) reduces inflammation by activating α7 nicotinic acetylcholine receptors (α7nAChRs) on macrophages, suppressing proinflammatory cytokines. Acetylcholinesterase inhibitors (AChEis), primarily used for Alzheimer's disease (AD), may exert anti‐inflammatory effects through the CAP. One AChEi, galantamine, also directly agonizes α7nAChRs, potentially enhancing its anti‐inflammatory properties. Objective: This study aimed to investigate the association between AChEi use, particularly galantamine, and T2DM risk in AD patients. Methods: We conducted a retrospective analysis of Veterans Health Administration (VA) data, examining early‐ and late‐onset AD patients receiving galantamine or other AD medications. Propensity score matching was used to balance groups and minimize confounding. Cox proportional hazard models assessed T2DM risk for galantamine, other AChEis and memantine. Results: A total of 40 065 AD patients were included in the study. Among early‐onset AD patients, galantamine use significantly reduced T2DM risk (hazard ratio [HR] = 0.80, 95% confidence interval [CI]: 0.66–0.98). Memantine also showed a protective effect (HR = 0.82, 95% CI: 0.69–1) in this group. Neither galantamine nor memantine influenced T2DM risk in late‐onset AD. Other AD medications showed no association with T2DM risk. Conclusion: Galantamine use was associated with a lower risk of T2DM in early‐onset AD patients, potentially due to enhanced anti‐inflammatory effects through both AChE inhibition and direct α7nAChR agonism. Memantine also demonstrated a protective effect. These findings suggest potential new applications for existing AD medications in T2DM prevention, particularly in early‐onset AD patients. Further research, including randomized controlled trials with diverse populations, is needed to confirm these results and the underlying mechanisms.

Background: The decreased mortality of people living with HIV (PLWH) has revealed non-HIV-associated comorbidities such as neurocognitive disorders (e.g., dementia). There is an urgency to discover therapeutics to prevent or delay neurocognitive decline among PLWH. Methods: The artificial intelligence platform Automatic Graph-mining And Transformer based Hypothesis Generation Approach (AGATHA) was utilized to seek potential drugs to be repurposed for the management of non-HIV-associated dementia. AGATHA revealed angiotensin-converting enzyme inhibitors that cross the blood-brain barrier (BBB ACEi) as a target for decreasing dementia. Subsequently, we conducted a retrospective study evaluating incident dementia using the VA Informatics and Computing Infrastructure (VINCI) evaluating ACE inhibitors. Cox proportional hazards models were fit and hazard ratios (HR) with corresponding 95% confidence intervals (CIs) are presented. Findings: A total 9,419 PLWH exposed to an BBB ACE inhibitor (ACEi) and 8,831 PLWH unexposed demonstrated that PLWH exposed to BBB ACEi had a 21.4% (univariate) and 15.2% (multivariate) lower hazard of dementia. The propensity score matched analysis demonstrated a 14.3% lower hazard of incident dementia compared to BBB ACEi unexposed (HR 0.857, 95% CI 0.747–0.984). Interpretation: An artificial intelligence-based literature mining system (AGATHA) was utilized to uncover a medication with potential to be repurposed. AGATHA demonstrated that BBB ACEi as a target for decreasing dementia among PLWH. Additionally, we conducted a retrospective study demonstrating a decrease in incident dementia among PLWH exposed to BBB ACEi. Future research is needed to explore further and understand the relationship of dementia among PLWH exposed to ACEi. Export

Neurodegenerative pathologies such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing. Author Summary This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.