Decentralized Clinical Trials: Scientific Considerations Through the Lens of the Estimand Framework.

While industry and regulators' interest in decentralized clinical trials (DCTs) is long-standing, the Covid-19 pandemic accelerated and broadened the adoption and experience with these trials. The key idea in decentralization is bringing the clinical trial design, typically on-site, closer to the patient's experience (on-site or off-site). Thus, potential benefits of DCTs include reducing the burden of participation in trials, broadening access to a more diverse population, or using innovative endpoints collected off-site. This paper helps researchers to carefully evaluate the added value and the implications of DCTs beyond the operational aspects of their implementation. The proposed approach is to use the ICH E9(R1) estimand framework to guide the strategic decisions around each decentralization component. Furthermore, the framework can guide the process for clinical trialists to systematically consider the implications of decentralization, in turn, for each attribute of the estimand. We illustrate the use of this approach with a fully DCT case study and show that the proposed systematic process can uncover the scientific opportunities, assumptions, and potential risks associated with a possible use of decentralization components in the design of a trial. This process can also highlight the benefits of specifying estimand attributes in a granular way. Thus, we demonstrate that bringing a decentralization component into the design will not only impact estimators and estimation but can also correspond to addressing more granular questions, thereby uncovering new target estimands.

Democratizing data at Novartis through clinical trial data access.

Enabling broad access and usage of clinical trial data within biopharmaceutical companies has historically been impeded by technical, cultural, and policy hurdles. Novartis has attempted to address this comprehensively through a program called data42; here, we explore how a diverse set of enterprise-wide stakeholders formulated a risk-based data access approach to streamline access to anonymized clinical trial data and vastly improved its use by authorized research and development (R&D) associates within the company. The result is that most Novartis clinical trial data requests, from internal associates, can now be automatically approved. The process of developing this framework and its impact on Novartis and the broader industry are explored and discussed.

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis.

BACKGROUND: Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. METHOD: The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the "IL-17" project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). RESULTS: A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. CONCLUSIONS: An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

Evaluating the re-identification risk of a clinical study report anonymized under EMA Policy 0070 and Health Canada Regulations.

BACKGROUND: Regulatory agencies, such as the European Medicines Agency and Health Canada, are requiring the public sharing of clinical trial reports that are used to make drug approval decisions. Both agencies have provided guidance for the quantitative anonymization of these clinical reports before they are shared. There is limited empirical information on the effectiveness of this approach in protecting patient privacy for clinical trial data. METHODS: In this paper we empirically test the hypothesis that when these guidelines are implemented in practice, they provide adequate privacy protection to patients. An anonymized clinical study report for a trial on a non-steroidal anti-inflammatory drug that is sold as a prescription eye drop was subjected to re-identification. The target was 500 patients in the USA. Only suspected matches to real identities were reported. RESULTS: Six suspected matches with low confidence scores were identified. Each suspected match took 24.2 h of effort. Social media and death records provided the most useful information for getting the suspected matches. CONCLUSIONS: These results suggest that the anonymization guidance from these agencies can provide adequate privacy protection for patients, and the modes of attack can inform further refinements of the methodologies they recommend in their guidance for manufacturers.

Challenges in Assessing the Impact of the COVID-19 Pandemic on the Integrity and Interpretability of Clinical Trials.

Abstract-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.

Protecting patient privacy when sharing patient-level data from clinical trials.

BACKGROUND: Greater 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. DISCUSSION: Our key recommendations are as follows: 1. 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. 2. 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. This 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.

Best practice for analysis of shared clinical trial data.

BACKGROUND: Greater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention. DISCUSSION: In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas. Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.

EFSPI/PSI working group on data sharing: accessing and working with pharmaceutical clinical trial patient level datasets - a primer for academic researchers.

BACKGROUND: Access 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. METHODS: Patient 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. RESULTS: The 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. CONCLUSION: Whilst 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.

Epigenetic modification of the FMR1 gene in fragile X syndrome is associated with differential response to the mGluR5 antagonist AFQ056.

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.

Lumiracoxib does not affect methotrexate pharmacokinetics in rheumatoid arthritis patients.

BACKGROUND: Methotrexate and nonsteroidal antiinflammatory drugs are frequently coadministered in the treatment of rheumatoid arthritis (RA). OBJECTIVE: To evaluate the effect of lumiracoxib, a novel cyclooxygenase-2 selective inhibitor, on methotrexate pharmacokinetics and short-term safety in patients with RA. METHODS: This multicenter, randomized, double-blind, placebo-controlled crossover study enrolled 18 patients (mean age 49.1 y) with stable RA. Patients were randomized to receive methotrexate 7.5-15 mg orally once weekly plus either lumiracoxib 400 mg/day or placebo for 7 days. Patients then received the other treatment combination for an additional 7 days. Serial blood and urine were collected for 24 hours after the methotrexate dose on day 1 (methotrexate alone) and days 8 and 15 (combination treatment). RESULTS: Plasma methotrexate pharmacokinetics (AUC(0-t), maximum concentration [C(max)], time to C(max)) and methotrexate protein binding were similar for methotrexate alone (108.0 ng.h/mL, 26.7 ng/mL, 1.5 h, and 57.1%, respectively), methotrexate/lumiracoxib (110.2 ng.h/mL, 27.5 ng/mL, 1.0 h, and 53.7%, respectively), and methotrexate/placebo (101.8 ng.h/mL, 22.6 ng/mL, 1.0 h, and 57.0%, respectively). Similarly, no clinically significant difference was found in the urinary excretion of methotrexate. Mean exposure to the 7-OH metabolite was lower when methotrexate was given with lumiracoxib compared with placebo, shown by a reduction in AUC and C(max), although similar amounts of the metabolite were recovered in urine following both lumiracoxib and placebo. Coadministration of methotrexate and lumiracoxib was well tolerated. CONCLUSIONS: Lumiracoxib had no significant effect on the pharmacokinetics, protein binding, or urinary excretion of coadministered methotrexate in patients with RA.

Assessment of lumiracoxib bioavailability from targeted sites in the human intestine using remotely activated capsules and gamma scintigraphy.

PURPOSE: To determine the bioavailability and pharmacokinetic profile of lumiracoxib from different sites in the gastrointestinal tract. METHODS: Subjects (11 healthy adult males) were randomized to receive a 100 mg lumiracoxib dose, via a site-specific radiolabeled delivery capsule, to the stomach (internal reference), proximal small bowel, distal small bowel, or ascending colon. Gamma scintigraphy was used for real-time visualization of capsule location, and a radiofrequency signal was used to activate capsules at target site. RESULTS: Ten subjects completed the study. The mean capsule activation times for the stomach, proximal small bowel, distal small bowel, and ascending colon were 0.22, 1.52, 3.43, and 11.46 h post dose, respectively. Lumiracoxib was well absorbed from the proximal and distal small bowel, with AUC(0-infinity) ratios 104% (86, 127)% and 110% (89, 136)%, respectively. The highest Cmax (2413 ng/ml) and AUC(0-infinity) for lumiracoxib were in the distal small bowel (6842 ng x h/ml). Effective absorption was observed from the ascending colon, with an AUC(0-infinity) ratio of 85% (69, 104)% vs. the reference. CONCLUSIONS: Lumiracoxib is rapidly and efficiently absorbed throughout the gastrointestinal tract.

Pharmacology and gastrointestinal safety of lumiracoxib, a novel cyclooxygenase-2 selective inhibitor: An integrated study.

BACKGROUND AND AIMS: Lumiracoxib is a structurally novel, acidic selective inhibitor of cyclooxygenase (COX)-2. We coordinated existing methodologies in a single study to evaluate potency, selectivity, and effect on the human gastrointestinal tract. METHODS: Twenty four healthy subjects (aged 18-45 years, 12 female) received high dose lumiracoxib (800 mg every day), standard dose naproxen (500 mg twice a day), or placebo for 8 days in a double-blind randomized crossover study. At the start and end of each dosing period, COX-2 selectivity was assessed by ex vivo serum thromboxane B(2) (COX-1) and lipopolysaccharide stimulated prostaglandin (PG) E(2) (COX-2), mucosal injury by endoscopy, and small and large bowel permeability by 0- to 5-hour and 5- to 24-hour (51)Cr-EDTA absorption. Plasma lumiracoxib was measured 2 hours after dosing on day 8 and vortex-stimulated ex vivo gastric mucosal PGE(2) synthesis at the end of each treatment period by enzyme immunoassay. RESULTS: Lumiracoxib was well absorbed and demonstrated similar potency to naproxen as a COX-2 inhibitor (77% and 66% inhibition, respectively, vs. placebo), but it differed in being more selective (24% and 97% inhibition of thromboxane B(2) vs. placebo). Gastric PGE(2) was reduced by 69% by naproxen (P < 0.001 vs. placebo) and 29% by lumiracoxib (P < 0.01 vs. placebo and naproxen). No subjects developed gastroduodenal erosions on lumiracoxib (vs. 75% on naproxen and 12.5% on placebo). (51)Cr-EDTA absorption increased significantly with naproxen but not lumiracoxib. CONCLUSIONS: Lumiracoxib is a potent selective inhibitor of COX-2 that causes little or no endoscopically detected stomach or duodenal injury or changes in bowel permeability.