Regulatory agencies can provide advice to support developers of digital technologies for medicines use, but what are the best strategies to maximize the chance of a successful regulatory interaction? Here, EMA and industry representatives comment on the experience so far.
Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THIS TOPIC? Decentralized clinical trials (DCTs) have the possibility to improve clinical trial conduct. However, regulatory requirements and perceived low degree of regulatory acceptance may impact the implementation of DCTs. WHAT QUESTION DID THIS STUDY ADDRESS? What are the opportunities and challenges for the authorization and implementation of DCTs in Europe from a regulators' perspective? WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Regulators expect that DCTs will facilitate the recruitment of underserved patients. Data collected in DCTs are furthermore expected to be more representative of the realworld. However, concerns regarding investigator oversight and safety monitoring may challenge DCT implementation. Regulators suggested that further experience with DCTs can be exerted through hybrid clinical trials, combining decentralized and on-site activities. HOW MIGHT THIS CHANGE CLINICAL PHARMA-COLOGY OR TRANSLATIONAL SCIENCE? This research helps progress the implementation of DCTs by providing insights into the opportunities and challenges for its implementation from a European regulator's perspective. The themes described in this research should be considered when designing a DCT and could help to educate regulators on DCTs.
BackgroundData capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.MethodsCase report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.ResultsThe analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.ConclusionsCommon data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0259-3) contains supplementary material, which is available to authorized users.
Background Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. Methods Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities’ documents. Results Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval. Conclusion The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.
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