8 research outputs found
Cardiac Adverse Events and Remdesivir in Hospitalized Patients with COVID-19 : A Post Hoc Safety Analysis of the Randomized DisCoVeRy Trial
No full textBackground: We aimed to evaluate the cardiac adverse events (AEs) in hospitalized patients with Coronavirus Disease 2019 (COVID-19) receiving remdesivir plus standard of care (SoC) compared to SoC alone (control), as an association was noted in some cohort studies and disproportionality analyses of safety databases. Methods: This post-hoc safety analysis is based on data from the multicenter, randomized, open-label, controlled DisCoVeRy trial in hospitalized patients with COVID-19 (NCT04315948). Any first AE occurring between randomization and day 29 in the modified intention-to-treat (mITT) population randomized to either remdesivir or control group was considered. Analysis was performed using Kaplan-Meier survival curves and Kaplan-Meier estimates were calculated for event rates. Results: Cardiac AEs were reported in 46 (11.2%) of 410 and 48 (11.3%) of 423 patients in the mITT population (n = 833) enrolled in the remdesivir and control groups, respectively. The difference between both groups was not significant (HR 1.0, 95% CI 0.7-1.5, p = 0.98), even when evaluating serious and non-serious cardiac AEs separately. The majority of reports in both groups were of arrhythmic nature (remdesivir, 84.8%; control, 83.3%) and were associated with a favorable outcome. There was no significant difference between remdesivir and control groups in the occurrence of different cardiac AE subclasses, including arrhythmic events (HR 1.1, 95% CI: 0.7-1.7, p = 0.68). Conclusions: Remdesivir treatment was not associated with an increased risk of cardiac AEs, whether serious or not, and regardless of AE severity, compared to control, in patients hospitalized with moderate or severe COVID-19. This is consistent with the results of other randomized controlled trials and meta-analyses
Management of pharmacovigilance during the COVID‐19 pandemic crisis by the safety department of an academic sponsor: Lessons learnt and challenges from the EU DisCoVeRy clinical trial
No full textAbstract The current COVID‐19 pandemic was an exceptional health situation, including for drug use. As there was no known effective drug for COVID‐19 at the beginning of the pandemic, different drug candidates were proposed. In this article, we present the challenges for an academic Safety Department to manage the global safety of a European trial during the pandemic. The National Institute for Health and Medical Research (Inserm) conducted a European multicenter, open‐label, randomized, controlled trial involving three repurposed and one‐in development drugs (lopinavir/ritonavir, IFN‐β1a, hydroxychloroquine, and remdesivir) in adults hospitalized with COVID‐19. From 25 March 2020 to 29 May 2020, the Inserm Safety Department had to manage 585 Serious Adverse Events (SAEs) initial notification and 396 follow‐up reports. The Inserm Safety Department's staff was mobilized to manage these SAEs and to report Expedited safety reports to the competent authorities within the legal timeframes. More than 500 queries were sent to the investigators due to a lack of or incoherent information on SAE forms. At the same time, the investigators were overwhelmed by the management of patients suffering from COVID‐19 infection. These particular conditions of missing data and lack of accurate description of adverse events made evaluation of the SAEs very difficult, particularly the assessment of the causal role of each investigational medicinal product. In parallel, working difficulties were accentuated by the national lockdown, frequent IT tool dysfunctions, delayed implementation of monitoring and the absence of automatic alerts for SAE form modification. Although COVID‐19 is a confounding factor per se, the delay in and quality of SAE form completion and the real‐time medical analysis by the Inserm Safety Department were major issues in the quick identification of potential safety signals. To conduct a high‐quality clinical trial and ensure patient safety, all stakeholders must take their roles and responsibilities
Ensuring quality control in a COVID-19 clinical trial during the pandemic: The experience of the Inserm C20–15 DisCoVeRy study
No full textInternational audienceSetting: Health measures taken during the pandemic deeply modified the clinical research practices. At the same time, the demand for the results of the COVID-19 trials was urgent. Thus, the objective of this article is to share Inserm's experience in ensuring quality control in clinical trials in this challenging context. Objectives: DisCoVeRy is a phase III randomized study that aimed at evaluating the safety and efficacy of 4 therapeutic strategies in hospitalized COVID-19 adult patients. Between March, 22nd 2020 and January, 20th 2021, 1309 patients were included. In order to guarantee the best quality of data, the Sponsor had to adapt to the current sanitary measures and to their impact on clinical research activity, notably by adapting Monitoring Plan objectives, involving the research departments of the participating hospitals and a network of clinical research assistants (CRAs). Results: Overall, 97 CRAs were involved and performed 909 monitoring visits. The monitoring of 100% of critical data for all patients included in the analysis was achieved, and despite of the pandemic context, a conform consent was recovered for more than 99% of patients. Results of the study were published in May and September 2021. Discussion/conclusion: The main monitoring objective was met thanks to the mobilization of considerable personnel resources, within a very tight time frame and external hurdles. There is a need for further reflection to adapt the lessons learned from this experience to the context of routine practice and to improve the response of French academic research during a future epidemic
Méthodes de partitionnements pour détecter des structures fines de population et applications au projet POPGEN
No full textInternational audienceIntroduction: To identify genetic risk factors for multifactorial disease, it is essential to compare the genomes of patients with those of genetically similar healthy individuals. It is therefore crucial to understand the genetic structure of the overall population. One important way of gaining such understanding is by applying clustering methods whose aim is to identify groups of individuals based on their genomes.Methods: In this context, we present a comparative analysis of various clustering approaches, with a focus on hierarchical methods such as fineSTRUCTURE, model-based clustering approaches such as Mclust, and aggregation-based clustering techniques. We also investigate the impact of different similarity measures obtained through haplotype-sharing methods on clustering outcomes.Results: We enhance previous comparative studies by evaluating clustering methods in the context of fine-scale population structure by simulating data that aligns with the observed population structure in French populations. This approach enables us to gauge the robustness and accuracy of various methods using simulated datasets. Additionally, we apply these methods to real data from POPGEN, a project encompassing the entire metropolitan territory of France and aggregating precise genetic and geographical information from over 9,772 volunteers. We investigate how the genetic clusters observed in POPGEN correspond to the fine-scale geography within different regions of France.Conclusion: Our study serves to demonstrate the performance of different clustering approaches on both simulated and real datasets, offering insights to help choose the most suitable clustering methods for identifying fine-scale population structure.Funding: This work is funded by the French Ministry of Research for the POPGEN project in the framework of the French initiative for genomic medicine (Plan France Médecine Génomique 2025; PFMG 2025; https://pfmg2025.aviesan.fr). The CONSTANCES cohort benefits from grant ANR-11INBS-0002 from the French National Research Agency
Méthodes de partitionnements pour détecter des structures fines de population et applications au projet POPGEN
No full textInternational audienceIntroduction: To identify genetic risk factors for multifactorial disease, it is essential to compare the genomes of patients with those of genetically similar healthy individuals. It is therefore crucial to understand the genetic structure of the overall population. One important way of gaining such understanding is by applying clustering methods whose aim is to identify groups of individuals based on their genomes.Methods: In this context, we present a comparative analysis of various clustering approaches, with a focus on hierarchical methods such as fineSTRUCTURE, model-based clustering approaches such as Mclust, and aggregation-based clustering techniques. We also investigate the impact of different similarity measures obtained through haplotype-sharing methods on clustering outcomes.Results: We enhance previous comparative studies by evaluating clustering methods in the context of fine-scale population structure by simulating data that aligns with the observed population structure in French populations. This approach enables us to gauge the robustness and accuracy of various methods using simulated datasets. Additionally, we apply these methods to real data from POPGEN, a project encompassing the entire metropolitan territory of France and aggregating precise genetic and geographical information from over 9,772 volunteers. We investigate how the genetic clusters observed in POPGEN correspond to the fine-scale geography within different regions of France.Conclusion: Our study serves to demonstrate the performance of different clustering approaches on both simulated and real datasets, offering insights to help choose the most suitable clustering methods for identifying fine-scale population structure.Funding: This work is funded by the French Ministry of Research for the POPGEN project in the framework of the French initiative for genomic medicine (Plan France Médecine Génomique 2025; PFMG 2025; https://pfmg2025.aviesan.fr). The CONSTANCES cohort benefits from grant ANR-11INBS-0002 from the French National Research Agency
Méthodes de partitionnements pour détecter des structures fines de population et applications au projet POPGEN
No full textInternational audienceIntroduction: To identify genetic risk factors for multifactorial disease, it is essential to compare the genomes of patients with those of genetically similar healthy individuals. It is therefore crucial to understand the genetic structure of the overall population. One important way of gaining such understanding is by applying clustering methods whose aim is to identify groups of individuals based on their genomes.Methods: In this context, we present a comparative analysis of various clustering approaches, with a focus on hierarchical methods such as fineSTRUCTURE, model-based clustering approaches such as Mclust, and aggregation-based clustering techniques. We also investigate the impact of different similarity measures obtained through haplotype-sharing methods on clustering outcomes.Results: We enhance previous comparative studies by evaluating clustering methods in the context of fine-scale population structure by simulating data that aligns with the observed population structure in French populations. This approach enables us to gauge the robustness and accuracy of various methods using simulated datasets. Additionally, we apply these methods to real data from POPGEN, a project encompassing the entire metropolitan territory of France and aggregating precise genetic and geographical information from over 9,772 volunteers. We investigate how the genetic clusters observed in POPGEN correspond to the fine-scale geography within different regions of France.Conclusion: Our study serves to demonstrate the performance of different clustering approaches on both simulated and real datasets, offering insights to help choose the most suitable clustering methods for identifying fine-scale population structure.Funding: This work is funded by the French Ministry of Research for the POPGEN project in the framework of the French initiative for genomic medicine (Plan France Médecine Génomique 2025; PFMG 2025; https://pfmg2025.aviesan.fr). The CONSTANCES cohort benefits from grant ANR-11INBS-0002 from the French National Research Agency
Implementation of a centralized pharmacovigilance system in academic pan‐European clinical trials : experience from EU‐Response and conect4children consortia
No full textSetting-up a high quality, compliant and efficient pharmacovigilance (PV) system in multi-country clinical trials can be more challenging for academic sponsors than for companies. To ensure the safety of all participants in academic studies and that the PV system fulfils all regulations, we set up a centralized PV system that allows sponsors to delegate work on PV. This initiative was put in practice by our Inserm-ANRS MIE PV department in two distinct multinational European consortia with 19 participating countries: conect4children (c4c) for paediatrics research and EU-Response for Covid-19 platform trials. The centralized PV system consists of some key procedures to harmonize the complex safety processes, creation of a local safety officer (LSO) network and centralization of all safety activities. The key procedures described the safety management plan for each trial and how tasks were shared and delegated between all stakeholders. Processing of serious adverse events (SAEs) in a unique database guaranteed the full control of the safety data and continuous evaluation of the risk-benefit ratio. The LSO network participated in efficient regulatory compliance across multiple countries. In total, there were 1312 SAEs in EU-Response and 83 SAEs in c4c in the four trials. We present here the lessons learnt from our experience in four clinical trials. We managed heterogeneous European local requirements and implemented efficient communication with all trial teams. Our approach builds capacity for PV that can be used by multiple academic sponsors
Cardiac Adverse Events and Remdesivir in Hospitalized Patients with Coronavirus Disease 2019 (COVID-19): A Post Hoc Safety Analysis of the Randomized DisCoVeRy Trial
No full textInternational audienceBackground We aimed to evaluate the cardiac adverse events (AEs) in hospitalized patients with Coronavirus Disease 2019 (COVID-19) receiving remdesivir plus standard of care (SoC) compared to SoC alone (control), as an association was noted in some cohort studies and disproportionality analyses of safety databases. Methods This post-hoc safety analysis is based on data from the multicenter, randomized, open-label, controlled DisCoVeRy trial in hospitalized patients with COVID-19 (NCT04315948). Any first AE occurring between randomization and day 29 in the modified intention-to-treat (mITT) population randomized to either remdesivir or control group was considered. Analysis was performed using Kaplan-Meier survival curves and Kaplan-Meier estimates were calculated for event rates. Results Cardiac AEs were reported in 46 (11.2%) of 410 and 48 (11.3%) of 423 patients in the mITT population (n = 833) enrolled in the remdesivir and control groups, respectively. The difference between both groups was not significant (HR 1.0, 95% CI 0.7-1.5, p = 0.98), even when evaluating serious and non-serious cardiac AEs separately. The majority of reports in both groups were of arrhythmic nature (remdesivir, 84.8%; control, 83.3%) and were associated with a favorable outcome. There was no significant difference between remdesivir and control groups in the occurrence of different cardiac AE subclasses, including arrhythmic events (HR 1.1, 95% CI: 0.7-1.7, p = 0.68). Conclusions Remdesivir treatment was not associated with an increased risk of cardiac AEs, whether serious or not, and regardless of AE severity, compared to control, in patients hospitalized with moderate or severe COVID-19. This is consistent with the results of other randomized controlled trials and meta-analyses
