With the growing demand for new medicines to meet critical healthcare needs with speed and efficiency, it has become essential to explore novel approaches and data sources. Thanks to the prevailing digital revolution, and advances in predictive analytics and computing platforms, a new frontier has emerged to enhance the drug development process. We elucidate pertinent aspects of the use of real-world evidence in regulatory settings, with emphasis on study design, analytical strategies, data quality, and regulatory requirements

This presentation will explore how to bridge gaps between clinical research and clinical care to leverage the power of healthcare data to bring medicines to patients faster and reduce the burden place on researchers through the use of HL7 FHIR. 

The electronic health record can be used and modified at the point of care to capture intentionally collected data for prospective research use cases. "Intentionally collected data" is data that is collected for the purpose of research and not per standard of care. We'll discuss a real example of how this was done in support of oncology clinical trials being run in the community setting.

• Clinical trial success can be enhanced by finding the right patients - those more likely to qualify, more likely to respond, and less likely to experience adverse events
• Trained on deep longitudinal data, AI algorithms can isolate these characteristics and identify patients most likely to have them
• Successful 'last-mile' deployment of these algorithms is finally possible - and is critical to achieving actual real-world benefit

The real-world data hype caused high expectations, including RCTs, might only play a minor role in future drug development. But they are rather complementary to RCT data and cannot replace them. Artificial intelligence may change drug development and time to market significantly, but will not replace past knowledge and experience. Real-World Evidence generation can be enhanced by AI and is key for public health.

To keep up with the rapidly changing environment and to emerge as a winner, life science enterprises need to embrace patient-centricity that provides longitudinal, comprehensive, and real-time Real World Data. Faster enrollment through automated, patient-mediated data collection leads to Real World Evidence as well as a decrease in time, cost and resources for clinical trials. Come see how Seqster is redefining Real World data collection through the patient.

Considerable progress has been made in the ability to draw causal inferences from analyses of real world data (RWD). Machine learning (ML) methods provide powerful analysis tools that have traditionally been used for prediction and classification problems. However, they also show promise for outperforming traditional statistical causal inference methods if used in the context of a causal framework.  This presentation will provide an overview of the use of ML methods for causal inference in epidemiology.

Decentralization has become a central strategy for conducting clinical research over the past few years, and while the focus has often centered on clinical trials, the benefits of decentralization are magnified in the generation of real-world evidence (RWE). This session will focus on how decentralized research technologies can both optimize prospective RWE generation while creating opportunities to combine with existing RWD to provide a more comprehensive view of the research participant.

Sponsors and CROs need more than change management to operationalize decentralized trials. In this session, Kyle Hogan will outline the approaches sponsors and CROs can take to build value in the decentralized trial participant journey, from pre-implementation to study launch and monitoring. 

Delays in clinical trial patient enrollment are significant challenges for sponsors, especially for biomarker-targeted investigational therapies associated with rare genomic variants. Join us and discover the solutions offered by SOPHiA GENETICS, leveraging insights from multimodal datasets - including 770’000 genomic profiles - curated across 780+ healthcare institutions. We can better enable biomarker-driven clinical trials in oncology and inherited rare disorders to meet recruitment goals by identifying potential sites worldwide.

The variety and fragmentation of healthcare data across institutions and software programs continue to pose a challenge for researchers to select “fit-for-purpose” real-world data (RWD). We designed a Use-case specific Relevance and Quality Assessment (UReQA) framework1 to evaluate the fitness of commercial RWD offerings in the US and implemented Oncology use cases of real-world time-to-treatment discontinuation. We are expanding the framework to support more use cases and additional therapeutical areas.

A number of studies have been conducted to examine if real-world evidence (RWE) generated from real-world data (RWD) can replicate findings from randomized clinical trials (RCTs). In addition to asking such replication questions, researchers may ask what can be learned from the differences between RCT evidence and RWE, and decision-makers may even ask whether such differences can be predicted. This presentation will cover several use cases across the Pharma, Payer/HTA, and regulatory settings that require different questions to be addressed.

Despite an abbreviated approval pathway, a relatively small number of biosimilars have been approved and an even smaller number have entered the marketplace. Slow uptake has stakeholders looking for strategies to optimize adoption in the future. This session will discuss real-world data research using medical and pharmacy claims to reveal the proportion of prescriptions for originator product versus biosimilar as well as switching patterns.

This session will focus on TransCelerate’s work on a forthcoming proposal for a quality management checklist to support audits of RWD used for regulatory decision-making.

Our industry is rightfully focused on the importance of diversity, equity, and inclusion in clinical trials. Many of us have been focused on this in our work and/or in our advocacy, both inside and outside of our organizations for some time. Why is inclusivity so important to PIs and patients? Why is it both a moral and a business imperative? Learn why representation in clinical research matters for your patients and how it shapes good science. The face of the world is changing and your success is tied to reaching ethnic minorities.