Frequency Domain Deep Learning With Non-Invasive Features for Intraoperative Hypotension Prediction.

BACKGROUND: Intraoperative hypotension can lead to postoperative organ dysfunction. Previous studies primarily used invasive arterial pressure as the key biosignal for the detection of hypotension. However, these studies had limitations in incorporating different biosignal modalities and utilizing the periodic nature of biosignals. To address these limitations, we utilized frequency-domain information, which provides key insights that time-domain analysis cannot provide, as revealed by recent advances in deep learning. With the frequency-domain information, we propose a deep-learning approach that integrates multiple biosignal modalities. METHODS: We used the discrete Fourier transform technique, to extract frequency information from biosignal data, which we then combined with the original time-domain data as input for our deep learning model. To improve the interpretability of our results, we incorporated recent interpretable modules for deep-learning models into our analysis. RESULTS: We constructed 75,994 segments from the data of 3,226 patients to predict hypotension during surgery. Our proposed frequency-domain deep-learning model outperformed conventional approaches that rely solely on time-domain information. Notably, our model achieved a greater increase in AUROC performance than the time-domain deep learning models when trained on non-invasive biosignal data only (AUROC 0.898 [95% CI: 0.885-0.91] vs. 0.853 [95% CI: 0.839-0.867]). Further analysis revealed that the 1.5-3.0 Hz frequency band played an important role in predicting hypotension events. CONCLUSION: Utilizing the frequency domain not only demonstrated high performance on invasive data but also showed significant performance improvement when applied to non-invasive data alone. Our proposed framework offers clinicians a novel perspective for predicting intraoperative hypotension.

Nonthermal Plasma-Stimulated C-N Coupling from CH4 and N2 Depends on the Presence of Surface CHx and Plasma-Phase CN Species.

Formation of C-N containing compounds from plasma-catalytic coupling of CH4 and N2 over various transition metals (Ni, Pd, Cu, Ag, and Au) is investigated using a multimodal spectroscopic approach, combining polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS) and optical emission spectroscopy (OES). Through sequential experiments utilizing CH4 and N2 nonthermal plasmas, we minimize plasma-phase reactions and identify key intermediates for C-N coupling on metal surfaces. Results show that simultaneous CH4 and N2 exposure with plasma stimulation produces surface C-N species. However, N2-CH4 sequential exposure does not lead to C-N species formation, while CH4-N2 sequential exposure reveals the presence of CHx surface species and CN radical species as key precursors to C-N species formation. From further analysis using X-ray photoelectron spectroscopy and liquid chromatography-mass spectrometry, the influence of exposure conditions on the degree of nitrogen incorporation and the nature of C-N species formed were revealed. The work highlights the importance of surface chemistry and exposure conditions in surface C-N coupling with plasma stimulation.

Alzheimer's disease drug development pipeline: 2024.

INTRODUCTION: New therapies to prevent or delay the onset of symptoms, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. METHODS: We interrogated clinicaltrials.gov including all clinical trials assessing pharmaceutical therapies for AD active in on January 1, 2024. We used the Common Alzheimer's Disease Research Ontology (CADRO) to classify the targets of therapies in the pipeline. RESULTS: There are 164 trials assessing 127 drugs across the 2024 AD pipeline. There were 48 trials in Phase 3 testing 32 drugs, 90 trials in Phase 2 assessing 81 drugs, and 26 trials in Phase 1 testing 25 agents. Of the 164 trials, 34% (N = 56) assess disease-modifying biological agents, 41% (N = 68) test disease-modifying small molecule drugs, 10% (N = 17) evaluate cognitive enhancing agents, and 14% (N = 23) test drugs for the treatment of neuropsychiatric symptoms. DISCUSSION: Compared to the 2023 pipeline, there are fewer trials (164 vs. 187), fewer drugs (127 vs. 141), fewer new chemical entities (88 vs. 101), and a similar number of repurposed agents (39 vs. 40). Highlights: In the 2024 Alzheimer's disease drug development pipeline, there are 164 clinical trials assessing 127 drugs.The 2024 Alzheimer's disease drug development pipeline has contracted compared to the 2023 Alzheimer pipeline with fewer trials, fewer drugs, and fewer new chemical entities.Drugs in the Alzheimer's disease drug development pipeline target a wide array of targets; the most common processes targeted include neurotransmitter receptors, inflammation, amyloid, and synaptic plasticity.The total development time for a potential Alzheimer's disease therapy to progress from nonclinical studies to FDA review is approximately 13 years.

Observation and Characterization of Vibrationally Active Surface Species Accessed with Nonthermal Nitrogen Plasmas.

Polycrystalline Ni, Pd, Cu, Ag, and Au foils exposed to nonthermal plasma (NTP)-activated N2 are found to exhibit a vibrational feature near 2200 cm-1 in polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS) observations that are not present in the same materials exposed to N2 under nonplasma conditions. The feature is similar to that reported elsewhere and is typically assigned to chemisorbed N2. We employ a combination of temperature-dependent experiments, sequential dosing, X-ray photoelectron spectroscopy, isotopic labeling, and density functional theory calculations to characterize the feature. Results are most consistent with a triatomic species, likely NCO, with the C and O likely originating from ppm-level impurities in the ultrahigh-purity (UHP) Ar and/or N2 gas cylinders. The work highlights the potential for nonthermal plasmas to access adsorbates inaccessible thermally as well as the potential contributions of ppm-level impurities to corrupt the interpretation of plasma catalytic chemistry.

An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification.

Introduction: Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods: Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results: adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion: Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Protective Effects of Melatonin in High-Fat Diet-Induced Hepatic Steatosis via Decreased Intestinal Lipid Absorption and Hepatic Cholesterol Synthesis.

BACKGRUOUND: The preventative effect of melatonin on the development of obesity and the progression of fatty liver under a high-fat diet (HFD) has been well elucidated through previous studies. We investigated the mechanism behind this effect regarding cholesterol biosynthesis and regulation of cholesterol levels. METHODS: Mice were divided into three groups: normal chow diet (NCD); HFD; and HFD and melatonin administration group (HFD+M). We assessed the serum lipid profile, mRNA expression levels of proteins involved in cholesterol synthesis and reabsorption in the liver and nutrient transporters in the intestines, and cytokine levels. Additionally, an in vitro experiment using HepG2 cells was performed. RESULTS: Expression of hepatic sterol regulatory element-binding protein 2 (SREBP-2), 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), and low-density lipoprotein receptor (LDLR) demonstrated that melatonin administration significantly reduces hepatic cholesterol synthesis in mice fed an HFD. Expression of intestinal sodium-glucose transporter 1 (SGLT1), glucose transporter 2 (GLUT2), GLUT5, and Niemann-pick C1-like 1 (NPC1L1) demonstrated that melatonin administration significantly reduces intestinal carbohydrate and lipid absorption in mice fed an HFD. There were no differences in local and circulatory inflammatory cytokine levels among the NCD, HFD, and HFD+M group. HepG2 cells stimulated with palmitate showed reduced levels of SREBP, LDLR, and HMGCR indicating these results are due to the direct mechanistic effect of melatonin on hepatocytes. CONCLUSION: Collectively, these data indicate the mechanism behind the protective effects of melatonin from weight gain and liver steatosis under HFD is through a reduction in intestinal caloric absorption and hepatic cholesterol synthesis highlighting its potential in the treatment of obesity and fatty liver disease.

Preparation of Remote Plasma Atomic Layer-Deposited HfO2 Thin Films with High Charge Trapping Densities and Their Application in Nonvolatile Memory Devices.

Optimization of equipment structure and process conditions is essential to obtain thin films with the required properties, such as film thickness, trapped charge density, leakage current, and memory characteristics, that ensure reliability of the corresponding device. In this study, we fabricated metal-insulator-semiconductor (MIS) structure capacitors using HfO2 thin films separately deposited by remote plasma (RP) atomic layer deposition (ALD) and direct-plasma (DP) ALD and determined the optimal process temperature by measuring the leakage current and breakdown strength as functions of process temperature. Additionally, we analyzed the effects of the plasma application method on the charge trapping properties of HfO2 thin films and properties of the interface between Si and HfO2. Subsequently, we synthesized charge-trapping memory (CTM) devices utilizing the deposited thin films as charge-trapping layers (CTLs) and evaluated their memory properties. The results indicated excellent memory window characteristics of the RP-HfO2 MIS capacitors compared to those of the DP-HfO2 MIS capacitors. Moreover, the memory characteristics of the RP-HfO2 CTM devices were outstanding as compared to those of the DP-HfO2 CTM devices. In conclusion, the methodology proposed herein can be useful for future implementations of multiple levels of charge-storage nonvolatile memories or synaptic devices that require many states.

Alzheimer's disease drug development pipeline: 2023.

Introduction: Drugs that prevent the onset, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. Methods: We searched ClinicalTrials.gov for all current Phase 1, 2 and 3 clinical trials for AD and mild cognitive impairment (MCI) attributed to AD. We created an automated computational database platform to search, archive, organize, and analyze the derived data. The Common Alzheimer's Disease Research Ontology (CADRO) was used to identify treatment targets and drug mechanisms. Results: On the index date of January 1, 2023, there were 187 trials assessing 141 unique treatments for AD. Phase 3 included 36 agents in 55 trials; 87 agents were in 99 Phase 2 trials; and Phase 1 had 31 agents in 33 trials. Disease-modifying therapies were the most common drugs comprising 79% of drugs in trials. Twenty-eight percent of candidate therapies are repurposed agents. Populating all current Phase 1, 2, and 3 trials will require 57,465 participants. Discussion: The AD drug development pipeline is advancing agents directed at a variety of target processes. HIGHLIGHTS: There are currently 187 trials assessing 141 drugs for the treatment of Alzheimer's disease (AD).Drugs in the AD pipeline address a variety of pathological processes.More than 57,000 participants will be required to populate all currently registered trials.

CPR5-mediated nucleo-cytoplasmic localization of IAA12 and IAA19 controls lateral root development during abiotic stress.

Plasticity of the root system architecture (RSA) is essential in enabling plants to cope with various environmental stresses and is mainly controlled by the phytohormone auxin. Lateral root development is a major determinant of RSA. Abiotic stresses reduce auxin signaling output, inhibiting lateral root development; however, how abiotic stress translates into a lower auxin signaling output is not fully understood. Here, we show that the nucleo-cytoplasmic distribution of the negative regulators of auxin signaling AUXIN/INDOLE-3-ACETIC ACID INDUCIBLE 12 (AUX/IAA12 or IAA12) and IAA19 determines lateral root development under various abiotic stress conditions. The cytoplasmic localization of IAA12 and IAA19 in the root elongation zone enforces auxin signaling output, allowing lateral root development. Among components of the nuclear pore complex, we show that CONSTITUTIVE EXPRESSOR OF PATHOGENESIS-RELATED GENES 5 (CPR5) selectively mediates the cytoplasmic translocation of IAA12/19. Under abiotic stress conditions, CPR5 expression is strongly decreased, resulting in the accumulation of nucleus-localized IAA12/19 in the root elongation zone and the suppression of lateral root development, which is reiterated in the cpr5 mutant. This study reveals a regulatory mechanism for auxin signaling whereby the spatial distribution of AUX/IAA regulators is critical for lateral root development, especially in fluctuating environmental conditions.

Development and Validation of a Prediction Model for Need for Massive Transfusion During Surgery Using Intraoperative Hemodynamic Monitoring Data.

Importance: Massive transfusion is essential to prevent complications during uncontrolled intraoperative hemorrhage. As massive transfusion requires time for blood product preparation and additional medical personnel for a team-based approach, early prediction of massive transfusion is crucial for appropriate management. Objective: To evaluate a real-time prediction model for massive transfusion during surgery based on the incorporation of preoperative data and intraoperative hemodynamic monitoring data. Design, Setting, and Participants: This prognostic study used data sets from patients who underwent surgery with invasive blood pressure monitoring at Seoul National University Hospital (SNUH) from 2016 to 2019 and Boramae Medical Center (BMC) from 2020 to 2021. SNUH represented the development and internal validation data sets (n = 17 986 patients), and BMC represented the external validation data sets (n = 494 patients). Data were analyzed from November 2020 to December 2021. Exposures: A deep learning-based real-time prediction model for massive transfusion. Main Outcomes and Measures: Massive transfusion was defined as a transfusion of 3 or more units of red blood cells over an hour. A preoperative prediction model for massive transfusion was developed using preoperative variables. Subsequently, a real-time prediction model using preoperative and intraoperative parameters was constructed to predict massive transfusion 10 minutes in advance. A prediction model, the massive transfusion index, calculated the risk of massive transfusion in real time. Results: Among 17 986 patients at SNUH (mean [SD] age, 58.65 [14.81] years; 9036 [50.2%] female), 416 patients (2.3%) underwent massive transfusion during the operation (mean [SD] duration of operation, 170.99 [105.03] minutes). The real-time prediction model constructed with the use of preoperative and intraoperative parameters significantly outperformed the preoperative prediction model (area under the receiver characteristic curve [AUROC], 0.972; 95% CI, 0.968-0.976 vs AUROC, 0.824; 95% CI, 0.813-0.834 in the SNUH internal validation data set; P < .001). Patients with the highest massive transfusion index (ie, >90th percentile) had a 47.5-fold increased risk for a massive transfusion compared with those with a lower massive transfusion index (ie, <80th percentile). The real-time prediction model also showed excellent performance in the external validation data set (AUROC of 0.943 [95% CI, 0.919-0.961] in BMC). Conclusions and Relevance: The findings of this prognostic study suggest that the real-time prediction model for massive transfusion showed high accuracy of prediction performance, enabling early intervention for high-risk patients. It suggests strong confidence in artificial intelligence-assisted clinical decision support systems in the operating field.

Alzheimer's disease drug development pipeline: 2022.

Introduction: Alzheimer's disease (AD) represents a global health crisis. Treatments are needed to prevent, delay the onset, slow the progression, improve cognition, and reduce behavioral disturbances of AD. We review the current clinical trials and drugs in development for the treatment of AD. Methods: We searched the governmental website clinicaltrials.gov where are all clinical trials conducted in the United States must be registered. We used artificial intelligence (AI) and machine learning (ML) approaches to ensure comprehensive detection and characterization of trials and drugs in development. We use the Common Alzheimer's Disease Research Ontology (CADRO) to classify drug targets and mechanisms of action of drugs in the pipeline. Results: As of January 25, 2022 (index date for this study) there were 143 agents in 172 clinical trials for AD. The pipeline included 31 agents in 47 trials in Phase 3, 82 agents in 94 trials in Phase 2, and 30 agents in 31 trials in Phase 1. Disease-modifying therapies represent 83.2% of the total number of agents in trials; symptomatic cognitive enhancing treatments represent 9.8% of agents in trials; and drugs for the treatment of neuropsychiatric symptoms comprise 6.9%. There is a diverse array of drug targets represented by agents in trials including nearly all CADRO categories. Thirty-seven percent of the candidate agents in the pipeline are repurposed drugs approved for other indications. A total of 50,575 participants are needed to fulfill recruitment requirements for all currently active clinical trials. Discussion: The AD drug development pipeline has agents representing a substantial array of treatment mechanisms and targets. Advances in drug design, outcome measures, use of biomarkers, and trial conduct promise to accelerate the delivery of new and better treatments for patients with AD. Highlights: There are 143 drugs in the current Alzheimer's disease (AD) drug development pipeline.Disease-modifying therapies represent 83.2% of the candidate treatments.Current trials require 50,575 participants who will donate 3,878,843 participant-weeks to clinical trials.The biopharmaceutical industry sponsors 50% of all clinical trials including 68% of Phase 3 trials.Sixty-three percent of Phase 3 trials and 46% of Phase 2 trials include non-North American clinical trial site locations indicating the global ecosystem required for AD drug development.

Direct Observation of Plasma-Stimulated Activation of Surface Species Using Multimodal In Situ/Operando Spectroscopy Combining Polarization-Modulation Infrared Reflection-Absorption Spectroscopy, Optical Emission Spectroscopy, and Mass Spectrometry.

Nonthermal plasmas (NTPs) produce reactive chemical environments, including electrons, ions, radicals, and vibrationally excited molecules, that can drive chemistry at temperatures at which such species are thermally inaccessible. There has been growing interest in the integration of conventional catalysis with reactive NTPs to promote novel chemical transformations. Unveiling the full potential of plasma-catalytic processes requires a comprehensive understanding of plasma-catalytic synergies, including characterization of plasma-catalytic surface interactions. In this work, we report on a newly designed multimodal spectroscopic instrument combining polarization-modulation infrared reflection-absorption spectroscopy (PM-IRAS), mass spectrometry, and optical emission spectroscopy (OES) for the investigation of plasma-surface interactions such as those found in plasma catalysis. In particular, this tool has been utilized to correlate plasma-phase chemistry with both surface chemistry and gas-phase products in situ (1) during the deposition of carbonaceous surface species via NTP-promoted nonoxidative coupling of methane and (2) during subsequent activation of surface deposits with an atmospheric pressure and temperature argon plasma jet on both nickel (Ni) and silicon dioxide (SiO2) surfaces. For the first time, the activation of carbonaceous surface species by a NTP on Ni and SiO2 surfaces to form hydrogen gas and C2 hydrocarbons was directly observed, where both PM-IRAS and OES measurements suggest that they may form through different pathways. This unique tool for studying plasma-surface interactions could enable more rational design of plasma-stimulated catalytic processes.

α2δ-1 switches the phenotype of synaptic AMPA receptors by physically disrupting heteromeric subunit assembly.

Many neurological disorders show an increased prevalence of GluA2-lacking, Ca2+-permeable AMPA receptors (CP-AMPARs), which dramatically alters synaptic function. However, the molecular mechanism underlying this distinct synaptic plasticity remains enigmatic. Here, we show that nerve injury potentiates postsynaptic, but not presynaptic, CP-AMPARs in the spinal dorsal horn via α2δ-1. Overexpressing α2δ-1, previously regarded as a Ca2+ channel subunit, augments CP-AMPAR levels at the cell surface and synapse. Mechanistically, α2δ-1 physically interacts with both GluA1 and GluA2 via its C terminus, inhibits the GluA1/GluA2 heteromeric assembly, and increases GluA2 retention in the endoplasmic reticulum. Consequently, α2δ-1 diminishes the availability and synaptic expression of GluA1/GluA2 heterotetramers in the spinal cord in neuropathic pain. Inhibiting α2δ-1 with gabapentin or disrupting the α2δ-1-AMPAR complex fully restores the intracellular assembly and synaptic dominance of heteromeric GluA1/GluA2 receptors. Thus, α2δ-1 is a pivotal AMPAR-interacting protein that controls the subunit composition and Ca2+ permeability of postsynaptic AMPARs.

Alzheimer's disease drug development pipeline: 2021.

INTRODUCTION: The number of individuals worldwide with Alzheimer's disease (AD) is growing at a rapid rate. New treatments are urgently needed. We review the current pipeline of drugs in clinical trials for the treatment of AD. METHODS: We interrogated ClinicalTrials.gov, the federal registry of clinical trials to identify drugs in trials. RESULTS: There are 126 agents in 152 trials assessing new therapies for AD: 28 treatments in Phase 3 trials, 74 in Phase 2, and 24 in Phase 1. The majority of drugs in trials (82.5%) target the underlying biology of AD with the intent of disease modification; 10.3% are putative cognitive enhancing agents; and 7.1% are drugs being developed to reduce neuropsychiatric symptoms. DISCUSSION: This pipeline analysis shows that target biological processes are more diversified, biomarkers are more regularly used, and repurposed agents are being explored to determine their utility for the treatment of AD.

Who funds Alzheimer's disease drug development?

INTRODUCTION: Despite the increase in Alzheimer's disease (AD) cases in the United States, no new treatments have been approved in the United States since 2003. The costs associated with drug development programs are high and serve as a significant deterrent to AD therapeutic investigations. In this study, we analyze the sponsorship data for AD clinical trials conducted since 2016 to assess the fiscal support for AD clinical trials. METHODS: We analyzed the funding sources of all AD trials over the past 5 years as reported on ClinicalTrials.gov. RESULTS: There were 136 trials being conducted for treatments in the US AD therapeutic pipeline on the index date of this study. Among non-prevention trials, disease-modifying therapies (DMT) in Phase 3 were almost entirely sponsored by the biopharmaceutical industry; Phase 2 DMT trials were split between the biopharmaceutical industry and funding from the National Institutes of Health (NIH) to academic medical centers (AMCs). The majority of prevention trials received sponsorship from public-private partnerships (PPP). Trials of symptomatic agents are equally likely to have biopharmaceutical or NIH/AMC sponsorship. Most trials with repurposed agents had NIH/AMC funding (89%). Since 2016, there has been consistent growth in the number of trials sponsored both in part and fully by NIH/AMC sources and in PPP, and there has been a reduction in biopharmaceutical company-sponsored trials. DISCUSSION: The number of trials supported by the biopharmaceutical industry has decreased over the past 5 years; trials supported from federal sources and PPP have increased. Repurposed compounds are mostly in Phase 2 trials and provide critical mechanistic information.

Bioelectrical impedance analysis for predicting postoperative complications and survival after liver resection for hepatocellular carcinoma.

BACKGROUND: Bioelectrical impedance analysis provides information on body composition and nutritional status. However, it's unclear whether the preoperative edema index or phase angle predicts postoperative complication or mortality in patients with hepatocellular carcinoma (HCC). Thus, we investigated whether preoperative bioelectrical impedance analysis could predict postoperative complications and survival in patients with HCC. METHODS: Seventy-nine patients who underwent hepatectomy for hepatocellular carcinoma were prospectively enrolled and bioelectrical impedance analysis was performed before surgery. Postoperative ascites or acute kidney injury and patients' survival were monitored after surgery. RESULTS: Among 79 patients, 35 (44.3%) developed ascites or acute kidney injury after hepatectomy. In multivariate analysis, a high preoperative edema index (extracellular water/total body water) (>0.384) (odds ratio 3.96; 95% confidence interval: 1.03-15.17; P=0.045) and higher fluid infusion during surgery (odds ratio 1.36; 95% confidence interval: 1.04-1.79; P=0.026) were identified as significant risk factors for ascites or acute kidney injury after hepatectomy. Subgroup analyses showed that the edema index was a significant predictor of ascites or acute kidney injury in patients with cirrhosis. Tumor size was the only significant predictive factor for short-term survival after hepatectomy. CONCLUSIONS: The preoperative edema index using bioelectrical impedance analysis can be used as a predictor of post-hepatectomy complication, especially in patients with liver cirrhosis.

Repurposed agents in the Alzheimer's disease drug development pipeline.

BACKGROUND: Treatments are needed to address the growing prevalence of Alzheimer's disease (AD). Clinical trials have failed to produce any AD drugs for Food and Drug Administration (FDA) approval since 2003, and the pharmaceutical development process is both time-consuming and costly. Drug repurposing provides an opportunity to accelerate this process by investigating the AD-related effects of agents approved for other indications. These drugs have known safety profiles, pharmacokinetic characterization, formulations, doses, and manufacturing processes. METHODS: We assessed repurposed AD therapies represented in Phase I, Phase II, and Phase III of the current AD pipeline as registered on ClinicalTrials.gov as of February 27, 2020. RESULTS: We identified 53 clinical trials involving 58 FDA-approved agents. Seventy-eight percent of the agents in trials had putative disease-modifying mechanisms of action. Of the repurposed drugs in the pipeline 20% are hematologic-oncologic agents, 18% are drugs derived from cardiovascular indications, 14% are agents with psychiatric uses, 12% are drug used to treat diabetes, 10% are neurologic agents, and the remaining 26% of drugs fall under other conditions. Intellectual property strategies utilized in these programs included using the same drug but altering doses, routes of administration, or formulations. Most repurposing trials were supported by Academic Medical Centers and were not funded through the biopharmaceutical industry. We compared our results to a European trial registry and found results similar to those derived from ClinicalTrials.gov. CONCLUSIONS: Drug repurposing is a common approach to AD drug development and represents 39% of trials in the current AD pipeline. Therapies from many disease areas provide agents potentially useful in AD. Most of the repurposed agents are generic and a variety of intellectual property strategies have been adopted to enhance their economic value.