In rare disease drug development and certain other areas of unmet medical need, RWD may be used to generate evidence for regulatory and related purposes. However, to ensure an adequate sample size for reliable inference, the data may need to be pooled from disparate sources. In such cases, it is essential to assess the combinability of the information from the different data sources. We review existing approaches and suggest recommendations, with particular emphasis on cases where either the sample size or the number of data sources is small.

The MGB Hospital Integrated Research Organization (HIRO) empowers industry sponsors, sites, and clinicians to accelerate AMC-based research by streamlining study start-ups, fostering collaborative workflows, and harnessing data-driven insights for patient-centric study designs. Our robust multi-modal real-world data platform combined with our unique approach to working with outside organizations enables us to better identify, engage, and recruit patients and successfully support sponsors from feasibility to trial performance.

Our present healthcare system focuses on treating people when they are ill rather than keeping them healthy. We have been using big data and remote monitoring approaches to monitor people while they are healthy to keep them that way and detect disease at its earliest moment presymptomatically. We use advanced multiomics technologies (genomics, immunomics, transcriptomics, proteomics, metabolomics, microbiomics) as well as wearables and microsampling for actively monitoring health. Following a group of 109 individuals for over 13 years revealed numerous major health discoveries covering cardiovascular disease, oncology, metabolic health and infectious disease. We have also found that individuals have distinct aging patterns that can be measured in an actionable period of time. Finally, we have used wearable devices for early detection of infectious disease, including COVID-19 as well as microsampling for monitoring and improving lifestyle. We believe that advanced technologies have the potential to transform healthcare and keep people healthy.

This presentation is designed to set the tone for the trial tokenization discussion and it will emphasize the role of various stakeholders involved in decision-making and process development around trial tokenization.

This presentation will elaborate on how clinical trial teams decide on which trials to tokenize and why.

There are several methods of tokenization available on the market. Which one is the right fit for your goals? This talk will summarize existing approaches and point out strengths and weaknesses of each of them.

How can we bring together clinical trials and RWD teams? Organizations that solve this problem will definitely win a competitive advantage in the near future.

This presentation will provide an overview of key learnings and challenges from implementation of trial tokenization and data linkage within a pragmatic randomized clinical trial, such as driving efficiencies with early collaborative engagement and operationalizing tokenization at a single versus multi-site level.

The adoption of innovations within drug development takes many years, and solving the manual entry problem for clinical data capture is no different, even with recent momentum on interoperability of data standards and software solutions for digital transfer of clinical data from site eSource to Sponsor EDC. Paul Jacobs will share how Regeneron is using an ongoing innovation-to-scale approach that considers technology, people, and process to de-risk adoption, and embracing the learnings, challenges, and failures as a key part of the process.

Biopharma companies are investing in real-world data (RWD) for a variety of uses. The potential to transform RWD into real-world evidence (RWE) to support regulatory decisions, including new product approvals or label expansions, is a key factor in the development of the RWD/RWE industry. This presentation will provide an overview of FDA regulations related to RWD and RWE and describe how the regulatory environment can influence this field. The presentation will focus on the 21st Century Cures Act and the subsequent FDA guidance documents and demonstration projects related to RWD and RWE.

This talk will help stakeholders understand and utilize one of the world’s most important data regions outside of the US, Scandinavia. Many practitioners and decision-makers know that this data is world-class, but few have the necessary information and tools to make informed decisions about where, when, why, and how to use this data. This talk will add a valuable resource to stakeholders’ tool belts regardless of whether their focus is commercial, regulatory, or academic.

The study investigates deriving external control arms (ECA) from real-world data (RWD) to enhance the value of single-arm Phase 1 trials. It applied various methods such as G-computation, digital twins, propensity score matching, and inverse probability treatment weighting. All methods reduced bias in projecting treatment effects in Phase 3 trials compared to no adjustment, with digital twins offering the least biased estimates.

The Oncology Research Biomarker Innovation Tower (ORBIT) establishes a cohesive and unified multimodal biomarker data hub across clinical studies as part of the Med.ai ecosystem. ORBIT enables accurate and real-time analysis of site, patient, sample, biomarker, and diagnostic data, providing actionable insights to study teams while transforming decision-making and streamlining operations.

What motivates pharma to go beyond the pill?  Emerging trends in Digital Biomarkers and Software-as-Medical-Device (SaMD) enabled the development of companion apps for commercial drugs. This approach allows pharma companies to remotely track medication side effects and monitor disease symptoms based on real-time digital measures otherwise not accessible using traditional RWE studies. What makes digital drug solutions successful? How are they regulated? What is the role of AI in empowering patients to make healthier choices?

This session will introduce synthetic data generation for health data. Case studies will be presented to show synthetic data as a privacy enhancing technology that facilitates data sharing and as a means of enhancing clinical trial data.

This presentation will share several case studies in various therapeutic areas of challenges and solutions of AI and ML implementation in clinical trial design.

Using artificial patients for drug development or medical device development is a promising field, only if the models can reach the required complexity and can be truly representative of the human population.