High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data
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ABSTRACT:
SUBMITTER: Zang C
PROVIDER: S-EPMC10713627 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundImmunocompromised individuals are at high risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and subsequent severe or fatal coronavirus disease 2019 (COVID-19), yet they have suboptimal responses to mRNA and inactivated COVID-19 vaccines. The efficacy of tixagevimab-cilgavimab in reducing symptomatic SARS-CoV-2 infection was demonstrated in phase III clinical trials. Nevertheless, real-world data on the effectiveness and safety of tixagevimab-cilgavimab remain limited.ObjectiveThe aim was to evaluate the effectiveness and safety of tixagevimab-cilgavimab among immunocompromised individuals.MethodsAdults who were immunocompromised or receiving immunosuppressive therapies were included in this target trial emulation using territory-wide electronic health records in Hong Kong. A sequential trial emulation approach was adopted to compare effectiveness and safety outcomes between individuals who received tixagevimab-cilgavimab and individuals who did not.ResultsA total of 746 tixagevimab-cilgavimab recipients and 2980 controls were included from 1 May 2022 to 30 November 2022. Tixagevimab-cilgavimab significantly reduced the risk of COVID-19 infection (hazard ratio [HR] 0.708, 95% confidence interval [CI] 0.527-0.951) during a median follow-up of 60 days. No significant difference was observed in the risk of COVID-19-related hospitalisation. Zero versus eight COVID-19 mortality cases and zero versus two severe COVID-19 cases were observed in tixagevimab-cilgavimab recipients and controls, respectively. Notably, significant risk reduction in COVID-19 infection was also observed among immunocompromised individuals who had been previously vaccinated with three or more doses of COVID-19 vaccine, or had no prior COVID-19 infection history.ConclusionsTixagevimab-cilgavimab was effective in reducing COVID-19 infection among immunocompromised patients during the Omicron wave. Findings were consistent among individuals who previously received three or more doses of COVID-19 vaccine, or had no previous history of COVID-19 infection.
| S-EPMC11399184 | biostudies-literature
Project description:Carfilzomib is a promising anticancer drug for relapsed/refractory multiple myeloma (RRMM). However, real-world evidence has only investigated the cardiovascular safety of carfilzomib, and there is a high demand for thorough safety evaluations. We aimed to comprehensively evaluate the risk of adverse events associated with carfilzomib in Korean patients with RRMM. We followed up with 138 matched patients with RRMM (69 KRd (carfilzomib, lenalidomide, and dexamethasone) and 69 Rd (lenalidomide and dexamethasone) users). A total of 12 adverse events were evaluated. More than 75% of adverse events occurred during the early cycle (1-6 cycles), and the incidence rate showed a tendency to decrease in the later cycle (7-12 and 13-18 cycles). Severities of most adverse events were evaluated as grade 1-2. The KRd regimen were related with significantly increased risks of dyspnea (adjusted HR (aHR) 2.27, 95% confidence interval (CI) 1.24-4.16), muscle spasm (aHR 5.12, 95% CI 1.05-24.9) and thrombocytopenia (aHR 1.84, 95% CI 1.10-3.06). Although the severities were low, carfilzomib has many side effects in treating RRMM; hence, findings on the patterns of its adverse events could lead to both effective and safe use of KRd therapy in real-world settings.
| S-EPMC9603615 | biostudies-literature
Project description:IntroductionEmerging preclinical evidence suggests that semaglutide, a glucagon-like peptide receptor agonist (GLP-1RA) for type 2 diabetes mellitus (T2DM) and obesity, protects against neurodegeneration and neuroinflammation. However, real-world evidence for its ability to protect against Alzheimer's disease (AD) is lacking.MethodsWe conducted emulation target trials based on a nationwide database of electronic health records (EHRs) of 116 million US patients. Seven target trials were emulated among 1,094,761 eligible patients with T2DM who had no prior AD diagnosis by comparing semaglutide with seven other antidiabetic medications. First-ever diagnosis of AD occurred within a 3-year follow-up period and was examined using Cox proportional hazards and Kaplan-Meier survival analyses.ResultsSemaglutide was associated with significantly reduced risk for first-time AD diagnosis, most strongly compared with insulin (hazard ratio [HR], 0.33 [95% CI: 0.21 to 0.51]) and most weakly compared with other GLP-1RAs (HR, 0.59 [95% CI: 0.37 to 0.95]). Similar results were seen across obesity status, gender, and age groups.DiscussionThese findings support further studies to assess semaglutide's potential in preventing AD.HighlightsSemaglutide was associated with 40% to 70% reduced risks of first-time AD diagnosis in T2DM patients compared to other antidiabetic medications, including other GLP-1RAs. Semaglutide was associated with significantly lower AD-related medication prescriptions. Similar reductions were seen across obesity status, gender, and age groups. Our findings provide real-world evidence supporting the potential clinical benefits of semaglutide in mitigating AD initiation and development in patients with T2DM. These findings support further clinical trials to assess semaglutide's potential in delaying or preventing AD.
| S-EPMC11667504 | biostudies-literature
Project description:Low-Density Lipoprotein (LDL) cholesterol is one of the main target for cardiovascular (CV) prevention and therapy. In the last years, Proprotein Convertase Subtilisin-Kexin type 9 inhibitors (PCSK9-i) has emerged as a key therapeutic target to lower LDL and were introduced for prevention of CV events. Recently (June 2022) the Italian Medicines Agency (AIFA) modified the eligibility criteria for the use of PCSK9-i. We designed an observational study to estimate the prevalence of eligible subjects and evaluate the effectiveness of PCSK9-i applying a Target Trial Emulation (TTE) approach based on Electronic Health Records (EHR). Subjects meeting the eligibility criteria were identified from July 2017 (when PCSK9-i became available) to December 2020. Outcomes were all-cause death and the first hospitalization. Among eligible subjects, we identified those treated at date of the first prescription. Inverse Probability of Treatment Weights (IPTW) were estimated including demographic and clinical covariates, history of treatment with statins and the month/year eligibility date. Competing risk models on weighted cohorts were used to derive the Average Treatment Effect (ATE) and the Conditional Average Treatment Effect (CATE) in subgroups of interest. Out of 1976 eligible subjects, 161 (8%) received treatment with PCSK9-i. Treated individuals were slightly younger, predominantly male, had more severe CV conditions, and were more often treated with statin compared to the untreated subjects. The latter exhibited a higher prevalence of non-CV comorbidities. A significant absolute and relative risk reduction of death and a lower relative risk for the first hospitalization was observed. The risk reduction for death was confirmed in CATE analysis. PCSk9-i were prescribed to a minority of eligible subjects. Within the TTE framework, the analysis confirmed the association between PCSK9-i and lower risk of events, aligning with findings from randomized clinical trials (RCTs). In our study, PCSK9-i provided protection specifically against all-cause death, expanding upon the evidence from RCTs that had primarily focused on composite CV outcomes.
| S-EPMC11341039 | biostudies-literature
Project description: Not available
| S-EPMC11826322 | biostudies-literature
Project description:Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer's disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: (1) Vanderbilt University Medical Center and (2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.
| S-EPMC10897392 | biostudies-literature
Project description:BackgroundReal-world data studies usually consider biases related to measured confounders. We emulate a target trial implementing study design principles of randomized trials to observational studies; controlling biases related to selection, especially immortal time; and measured confounders.MethodsThis comprehensive analysis emulating a randomized clinical trial compared overall survival in patients with HER2-negative metastatic breast cancer (MBC), receiving as first-line treatment, either paclitaxel alone or combined to bevacizumab. We used data from 5538 patients extracted from the Epidemiological Strategy and Medical Economics-MBC cohort to emulate a target trial using advanced statistical adjustment techniques including stabilized inverse-probability weighting and G-computation, dealing with missing data with multiple imputation, and performing a quantitative bias analysis for residual bias due to unmeasured confounders.ResultsEmulation led to 3211 eligible patients, and overall survival estimates achieved with advanced statistical methods favored the combination therapy. Real-world effect sizes were close to that assessed in the existing E2100 randomized clinical trial (hazard ratio = 0.88, P = .16), but the increased sample size allowed to achieve a higher level of precision in real-world estimates (ie, reduced confidence intervals). Quantitative bias analysis confirmed the robustness of the results with respect to potential unmeasured confounding.ConclusionTarget trial emulation with advanced statistical adjustment techniques is a promising approach to investigate long-term impact of innovative therapies in the French Epidemiological Strategy and Medical Economics-MBC cohort while minimizing biases and provides opportunities for comparative efficacy through the synthetic control arms provided.Database registrationclinicaltrials.gov Identifier NCT03275311.
| S-EPMC10407701 | biostudies-literature
Project description:Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Real-world data, such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework on a coronary artery disease cohort of millions of patients. We successfully identify drugs and drug combinations that substantially improve the coronary artery disease outcomes but haven't been indicated for treating coronary artery disease, paving the way for drug repurposing.
| S-EPMC9119409 | biostudies-literature
Project description: Not available
| S-EPMC11826321 | biostudies-literature
Project description:BackgroundRandomized controlled trials in pediatric kidney transplantation are hampered by low incidence and prevalence of kidney failure in children. Real-World Data from patient registries could facilitate the conduct of clinical trials by substituting a control cohort. However, the emulation of a control cohort by registry data in pediatric kidney transplantation has not been investigated so far.MethodsIn this multicenter comparative analysis, we emulated the control cohort (n = 54) of an RCT in pediatric kidney transplant patients (CRADLE trial; ClinicalTrials.gov NCT01544491) with data derived from the Cooperative European Paediatric Renal Transplant Initiative (CERTAIN) registry, using the same inclusion and exclusion criteria (CERTAIN cohort, n = 554).ResultsMost baseline patient and transplant characteristics were well comparable between both cohorts. At year 1 posttransplant, a composite efficacy failure end point comprising biopsy-proven acute rejection, graft loss or death (5.8% ± 3.3% vs. 7.5% ± 1.1%, P = 0.33), and kidney function (72.5 ± 24.9 vs. 77.3 ± 24.2 mL/min/1.73 m2 P = 0.19) did not differ significantly between CRADLE and CERTAIN. Furthermore, the incidence and severity of BPAR (5.6% vs. 7.8%), the degree of proteinuria (20.2 ± 13.9 vs. 30.6 ± 58.4 g/mol, P = 0.15), and the key safety parameters such as occurrence of urinary tract infections (24.1% vs. 15.5%, P = 0.10) were well comparable.ConclusionsIn conclusion, usage of Real-World Data from patient registries such as CERTAIN to emulate the control cohort of an RCT is feasible and could facilitate the conduct of clinical trials in pediatric kidney transplantation. A higher resolution version of the Graphical abstract is available as Supplementary information.
| S-EPMC9584233 | biostudies-literature