Fragile X syndrome (FXS) is an X-linked condition associated with intellectual disability and behavioral problems. It is caused by expansion of a CGG repeat in the 5' untranslated region of the fragile X mental retardation 1 (FMR1) gene. This mutation is associated with hypermethylation at the FMR1 promoter and resultant transcriptional silencing. FMR1 silencing has many consequences, including up-regulation of metabotropic glutamate receptor 5 (mGluR5)-mediated signaling. mGluR5 receptor antagonists have shown promise in preclinical FXS models and in one small open-label study of FXS. We examined whether a receptor subtype-selective inhibitor of mGluR5, AFQ056, improves the behavioral symptoms of FXS in a randomized, double-blind, two-treatment, two-period, crossover study of 30 male FXS patients aged 18 to 35 years. We detected no significant effects of treatment on the primary outcome measure, the Aberrant Behavior Checklist-Community Edition (ABC-C) score, at day 19 or 20 of treatment. In an exploratory analysis, however, seven patients with full FMR1 promoter methylation and no detectable FMR1 messenger RNA improved, as measured with the ABC-C, significantly more after AFQ056 treatment than with placebo (P < 0.001). We detected no response in 18 patients with partial promoter methylation. Twenty-four patients experienced an adverse event, which was mostly mild to moderately severe fatigue or headache. If confirmed in larger and longer-term studies, these results suggest that blockade of the mGluR5 receptor in patients with full methylation at the FMR1 promoter may show improvement in the behavioral attributes of FXS.

BackgroundGreater transparency and, in particular, sharing of patient-level data for further scientific research is an increasingly important topic for the pharmaceutical industry and other organisations who sponsor and conduct clinical trials as well as generally in the interests of patients participating in studies. A concern remains, however, over how to appropriately prepare and share clinical trial data with third party researchers, whilst maintaining patient confidentiality. Clinical trial datasets contain very detailed information on each participant. Risk to patient privacy can be mitigated by data reduction techniques. However, retention of data utility is important in order to allow meaningful scientific research. In addition, for clinical trial data, an excessive application of such techniques may pose a public health risk if misleading results are produced. After considering existing guidance, this article makes recommendations with the aim of promoting an approach that balances data utility and privacy risk and is applicable across clinical trial data holders.DiscussionOur key recommendations are as follows:Data anonymisation/de-identification: Data holders are responsible for generating de-identified datasets which are intended to offer increased protection for patient privacy through masking or generalisation of direct and some indirect identifiers.Controlled access to data, including use of a data sharing agreement: A legally binding data sharing agreement should be in place, including agreements not to download or further share data and not to attempt to seek to identify patients. Appropriate levels of security should be used for transferring data or providing access; one solution is use of a secure ‘locked box’ system which provides additional safeguards.SummaryThis article provides recommendations on best practices to de-identify/anonymise clinical trial data for sharing with third-party researchers, as well as controlled access to data and data sharing agreements. The recommendations are applicable to all clinical trial data holders. Further work will be needed to identify and evaluate competing possibilities as regulations, attitudes to risk and technologies evolve.

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The COVID-19 pandemic has a global impact on the conduct of clinical trials of medical products. This article discusses implications of the COVID-19 pandemic on clinical research methodology aspects and provides points to consider to assess and mitigate the risk of seriously compromising the integrity and interpretability of clinical trials. The information in this article will support discussions that need to occur cross-functionally on an ongoing basis to "integrate all available knowledge from the ethical, the medical, and the methodological perspective into decision making. " This article aims at facilitating: (i) risk assessments of the impact of the pandemic on trial integrity and interpretability; (ii) identification of the relevant data and information related to the impact of the pandemic on the trial that needs to be collected; (iii) short-term decision making impacting ongoing trial operations; (iv) ongoing monitoring of the trial conduct until completion, including the possible involvement of data monitoring committees, and adequately documenting all measures taken to secure trial integrity throughout and after the pandemic, and (v) proper analysis and interpretation of the eventual interim or final trial data.

SUMMARYBackground: Lumiracoxib (Prexige Ò ) is a cyclooxygenase-2 (COX-2) selective inhibitor. Aim: To compare the gastroduodenal tolerability of lumiracoxib with placebo and naproxen in a randomized, parallel-group, double-blind study. Methods: Sixty-five healthy male subjects were randomized to receive 8 days' dosing with lumiracoxib 200 mg twice daily (b.d.) (n ¼ 21), placebo (n ¼ 22) or naproxen 500 mg b.d. (n ¼ 22). Endoscopic evaluations of gastric and duodenal mucosae were conducted at baseline and after 8 days' dosing. Serum was assayed for ex-vivo concentrations of thromboxane B 2 (TxB 2 ) to determine cyclooxygenase-1 (COX-1) inhibitory activity. Results: Sixty subjects (20 per group) completed the study. No gastroduodenal erosions were observed in

Synthetic data is a rapidly evolving field with growing interest from multiple industry stakeholders and European bodies. In particular, the pharmaceutical industry is starting to realise the value of synthetic data which is being utilised more prevalently as a method to optimise data utility and sharing, ultimately as an innovative response to the growing demand for improved privacy. Synthetic data is data generated by simulation, based upon and mirroring properties of an original dataset. Here, with supporting viewpoints from across the pharmaceutical industry, we set out to explore use cases for synthetic data across seven key but relatable areas for optimising data utility for improved data privacy and protection. We also discuss the various methods which can be used to produce a synthetic dataset and availability of metrics to ensure robust quality of generated synthetic datasets. Lastly, we discuss the potential merits, challenges and future direction of synthetic data within the pharmaceutical industry and the considerations for this privacy enhancing technology.

BackgroundAccess to patient level datasets from clinical trial sponsors continues to be an important topic for the Pharmaceutical Industry as well as academic institutions and researchers. How to make access to patient level data actually happen raises many questions from the perspective of the researcher.MethodsPatient level data access models of all major pharmaceutical companies were surveyed and recommendations made to guide academic researchers in the most efficient way through the process of requesting and accessing patient level data.ResultsThe key considerations for researchers covered here are finding information; writing a research proposal to request data access; the review process; how data are shared; and the expectations of the data holder. A lot of clinical trial information is available on public registries and so these are great sources of information. Depending on the research proposal the required information may be available in Clinical Study Reports and therefore patient level data may not need to be requested. Many data sharing systems have an electronic form or template but in cases where these are not available the proposal needs to be created as a stand-alone document outlining the purpose, statistical analysis plan, identifying the studies for which data are required, the research team members involved, any conflicts of interest and the funding for the research.There are three main review processes - namely having an internal review board, external review board selected by the data holder or an external review board selected by a third party. Data can be shared through Open access i.e. on a public website, direct sharing between the data holder and the researcher, controlled access or the data holder identifies a contract organization to access the data and perform the analyses on behalf of the researcher. The data that are shared will have accompanying documentation to assist the researcher in understanding the original clinical trial and data collection methods. The data holder will require a legally binding data sharing agreement to be set up with the researcher. Additionally the data holder may be available to provide some support to the researcher if questions arise.ConclusionWhilst the benefits and value of patient level data sharing have yet to be fully realised, we hope that the information outlined in this article will encourage researchers to consider accessing and re-using clinical trial data to support their research questions.

Lumiracoxib had no significant effect on the pharmacokinetics, protein binding, or urinary excretion of coadministered methotrexate in patients with RA.