Citations

Abstract Alzheimer’s Disease (AD) 1 is a progressive neurodegenerative disorder with limited therapeutic options, driving interest in drug repurposing to accelerate treatment discovery. Drug repurposing has emerged as a promising strategy to accelerate therapeutic discovery by repositioning existing drugs for new clinical indications. Recent computational repurposing approaches, including knowledge graph reasoning, transcriptomic signature analysis, and integrative literature mining, have demonstrated strong predictive capabilities 2 . However, these methods often yield divergent drug rankings, which makes it difficult to decide which candidates to advance for experimental follow-up and results in substantial gaps between computational predictions and feasible in vivo validation 2 .To bridge this computational-experimental gap, we proposed an advanced prioritization framework leveraging large language models (LLMs). Our method systematically evaluated three state-of-the-art (SOTA) and representative computational methods (TxGNN 3 , Composition-based Graph Convolutional Network (CompGCN) 4 , and a regularized logistic regression (RLR) 5 , to analyze both their predictive performance and pharmaceutical class distributions. By integrating the strengths and divergences of these models, we generated a unified, streamlined list of 90 candidate drugs for further prioritization. We then utilized an LLM-based agent to perform evidence synthesis from biomedical literature abstracts for each candidate. This process mimics expert manual curation but significantly reduces human effort and time by efficiently distilling vast textual data into actionable insights. Applying consistent and transparent selection criteria, we obtained a refined and prioritized list of drug candidates suitable for subsequent in vivo experimental validation. The robustness and clinical relevance of our framework were validated using real-world data from Alzheimer’s patient cohorts, clinical trial registries, and expert pharmacological reviews. This comprehensive validation confirmed that our LLM-driven approach enhances efficiency, consistency, scalability, and generalizability. By integrating computational predictions with scalable evidence synthesis and multifaceted validation, our framework facilitated rapid and informed prioritization of repurposed drugs. Our framework can potentially accelerate the translational pathway toward viable AD therapeutics. Moreover, the versatility of our framework can also be applied to drug repurposing efforts for other diseases beyond AD.

Abstract Background The legality and use of marijuana is a controversial topic. With increased prevalence of usage, cannabis detection for legal, medical, and workforce drug testing is on the rise. LC-MS/MS quantification of delta 9-carboxy tetrahydrocannabinol (delta 9-THC-COOH) in urine is accepted as the standard to detect marijuana use. One challenge faced by testing laboratories has been identifying adulterated samples. Many products/reagents can be used to manipulate the urine to avoid positive drug testing results. In this study, we developed a dilute-and-shoot strategy to separate and quantify both the delta 8- and 9-THC-COOH in urine samples and tested the effects of adulteration using a combination of nitrite and/or acids which can be used to mask cannabinoid use. Methods Five authentic delta 8- and 9-THC-COOH positive specimens, and one spiked sample were treated with strong acids (1N HCL or 10% TCA), 500 µg/mL nitrite, or a combination. After hydrolysis with sodium hydroxide, the analytes were quantified using a 6495 Mass Spectrometer coupled to a StreamSelect LC system (Agilent). LC separation was performed with a CORTECS T3 column utilizing a shallow gradient. In addition, twelve urine specimens with varying concentrations of delta 8- and 9-THC-COOH were treated with nitrite and screened using a delta 9-THC-COOH immunoassay on the Roche Cobas C501 and analyzed by LC-MS/MS. Results Nitrite did not change the physical appearance or the pH of the urine samples. Strong acid treatment also did not alter the appearance but did drop the urine pH ≤2. Pre-treated specimens with 1N HCL, 10% TCA, or 500 µg/mL of nitrite did not affect the delta 8- and 9-THC-COOH concentrations up to 6 days (refrigerated); however, specimens treated with strong acid combined with 500 µg/mL of nitrite for 30 min made both analytes undetectable. Additional experiments confirmed that 0.5 and 1 mol/L of nitrite lowered the concentration of all analytes, including internal standard, to undetectable levels in 48 h. Twelve additional specimens were collected, 3 with delta 8-THC-COOH, 3 with delta 9-THC-COOH, and 6 with both. All 12 samples screened positive using the Roche immunoassay and were quantified using the LC-MS/MS method. After treatment with 0.5 mol/L of nitrite, 10 of the 12 samples were negative using the immunoassay, but 2 samples remained positive. However, all nitrite treated specimens were negative (<LLOQ) using the LC-MS/MS method and the internal standard was eliminated. Later experiments confirmed that the delta 8- and 9-THC-COOH could be eliminated with as little as 0.1 mol/L nitrite for 30 min. Conclusion The unique dilute-and-shoot method can separate and quantify delta 8- and 9-THC-COOH. In addition, high concentrations of nitrite were shown to degrade both analytes along with the isotopically labeled internal standard. As a result, labs can distinguish true negative delta 8- and 9-THC-COOH (no analyte peaks) from nitrite adulterated specimens since the adulterated samples have no peaks for both the analytes and internal standard. A limitation of the study is that only sodium nitrite was tested. Other adulterants, such as potassium nitrite, pyridinium chlorochromate, peroxide etc. require further investigation.

Abstract Background: The Phase III trial (NCT00337103) compared eribulin (E) with capecitabine (C) in patients (pts) with metastatic breast cancer (MBC) in the 1st-, 2nd-, and 3rd-line setting. Median overall survival (OS) was 15.9 and 14.5 months (HR 0.88; 95% CI 0.77, 1.00; P = 0.056) and median progression-free survival (PFS) was 4.1 and 4.2 months (HR 1.08; 95% CI 0.93, 1.25; P = 0.30) for E and C, respectively. This analysis compares toxicity and efficacy of E and C in relation to age. Material and methods: In this post-hoc exploratory analysis, the effect of age on the incidence of adverse events (AEs), OS, PFS, and objective response rate (ORR) with E and C were analyzed for two age groups: ≤65 years (E, n = 468; C, n = 491) and >65 years (E, n = 86; C, n = 57). For OS and PFS, analyses were stratified by HER2 and geographic region. Results: With increasing age, the proportion of pts with worse performance status (PS ≥1: 54.5% vs 69.2% for ≤65 and >65 years, respectively), ER+ (47.4% vs 57.3%), and PgR+ MBC (41.3% vs 45.5%) increased, and the proportion with triple-negative MBC decreased (26.6% vs 20.3%). With both treatments, AEs were reported in a higher proportion of pts in the older age group, this becoming more apparent for grade 3+ AEs (E: 64.6% vs 70.2%, and C: 45.0% vs 54.4% for ≤65 and >65 respectively). With E, there was a trend for increased incidence of grade 3/4 neutropenia (45.0% vs 50.0%) and leukopenia (13.7% vs 22.6%) but, in contrast, decreased peripheral sensory neuropathy (3.9% vs 1.2%) with increasing age. For C, there was a trend for increased palmar-plantar erythrodysethesia syndrome (total: 44.4% vs 50.9%; grade 3/4: 14.1% vs 17.5%), and grade 3/4 fatigue (1.8% vs 7.0%) and diarrhea (4.7% vs 10.5%) with increasing age; emesis and nausea were similar for both age groups. Dose adjustments due to AEs with E were slightly higher in the older age group: withdrawals 7.4% vs 10.7%; dose reductions 31.1% vs 36.9%; and dose delays 30.9% vs 36.9%. With C, there was a trend for an increased incidence of withdrawals (9.2% vs 21.1%) and dose delays (34.2% vs 49.1%) due to AEs with increasing age: the incidence of dose reductions was slightly higher in the older age group (31.3% vs 36.8%). In an unadjusted analysis, a trend for improved OS with E vs C was observed in both subgroups (≤65 years: median 15.8 vs 14.5 months; HR 0.90; 95% CI 0.78, 1.04; P = 0.16, and >65 years: median 18.4 vs 14.1 months; HR 0.74; 95% CI 0.50, 1.12; P = 0.16). PFS and ORR for E and C were: median PFS: E, 4.0 and 5.4 months; C, 4.2 and 5.9 months; ORR: E, 10.9% and 11.6%; C, 11.6% and 10.5%, in the ≤65 and >65 groups respectively. Conclusions: This exploratory and unadjusted analysis suggests a trend for improved OS with E in both younger and older pts with MBC. With both treatments there was a suggestion that AEs were reported in a higher proportion of pts in the older age group, this becoming more apparent for grade 3+ events. Specifically, these data suggest an increased incidence of grade 3/4 diarrhea, dose delays, and study withdrawal due to AEs in pts treated with C, and potentially suggest that with E there may be less difference between the AE profile in younger vs older pts than with C. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P3-13-04.