Gain the physician investigator and clinical site perspective on challenges in surgical trials, and how to incorporate site input into the protocol development process, optimize study protocol, build and implement a data-driven site selection approach on a global scale. This will include self-reflection about what factors in past and current trials led to success and failure of the trial at different stages.

The clinical research industry is changing, and the number of protocols requiring special populations for early-phase clinical studies is increasing. First-in-human (FIH) studies now routinely contain several parts with multiple cohorts with increased dose flexibility and accelerated dose escalation paradigms; each of which might have been previously a separate clinical trial. Sponsors, CROs, and investigators struggle to minimize risks and avoid serious consequences, and yet, there remains real risk in an FIH study. Understating challenges associated with the use of special populations is critical to ensure that the study design will allow for the timely and successful completion of the project, while minimizing individual exposure to the risks of participating subjects, managing costs, and assessing safety profile. This session will explore the challenges clinical teams face in developing protocols to ensure that the right patients are enrolled and that the right data are collected to demonstrate a drug is safe and efficacious, while at the same time, managing study costs and study complexity, especially in trials that involve early-phase drug metabolism, imaging, and interventional procedures. Key factors to consider, when developing protocols and techniques to minimize complexity while ensuring trial success, will be discussed. A methodology to identify operational processes at execution phase, and best practices to increase fiscal return and mitigate fiscal compliance risks for clinical trials with special population will be reviewed.

Potential to merge, overlay, and analyze data is proving to increase the ability to be predictive in achieving earlier and better outcomes in the area of protocol optimization. Janssen is using natural language processing in the development of benchmarks for computing patient burden and site burden, and the correlation of trial burden, trial costs, and actual enrollment with design parameters and amendments. Ultimately, this allows for protocol design with better consideration of patient and site voice, and fewer protocol amendments.

We’ve all experienced the frustration of an “ideal” sponsor protocol that is challenging for sites to execute or for patients to participate in. This panel discussion will provide interactive insights into how sites and industry can partner together more effectively to design relevant clinical trial protocols and optimize study execution for all stakeholders.

Ensuring the optimum site selection is critical for successful trials. This presentation will show how to leverage data and analytics to identify the most appropriate and highest performing sites for new studies. The processes, systems/databases, and best practises to analyse site data for the creation of the master site list will be discussed. Potential future analytics advances in global site selection will be reviewed.

Both sponsors and sites have a common goal--a successful trial. Success can only be achieved if multiple perspectives are considered. A collaborative approach in site selection leads to increased success in study enrollment. After being selected as a site by the sponsor, UofL's Clinical Trials Unit uses its own sponsor questionnaire to evaluate the project from the site perspective. The goal is to make sure that the study is the right one, both for the sponsor and the site.

The ICH revised E6 guidelines was issued to reflect on the current research landscape: increases in globalization, study complexity, and technological capabilities. The updated ICH GCP E6 R2 is more descriptive than the previous version and has 26 items of change. These changes consist of new items in definitions; new sections on investigator responsibilities, including oversight; a substantial new sponsor section on quality management, including risk assessment; monitoring plans defined and implemented; introducing Risk-Based Quality Management; serious breaches, and, a new section on computer validation and electronic records, to name a few.

We are in the age of information, but we don’t necessarily use, analyze, or share data in the most effective way. What would happen if pharma routinely participated in Master Trials managed by non-profits as an alternative to traditional clinical trials? Should it be a core element of their pipeline strategies? A centralized resource to quickly and efficiently examine drug candidates. What if our collaborative efforts would streamline decision making so that the most effective drugs could be fast-tracked into clinical trials? Could we save time, money, and other resources if our go/no-go decisions were made in months instead of years? This presentation will describe a novel approach to genomic-based decision-making to optimize breakthrough therapeutics. This drug development model combines scientific innovation and next-gen eclinical technologies to radically change the drug development landscape.

This discussion will clarify the definition of “protocol deviation”, review support classification of “important” and “non-important” protocol deviations, and provide a toolkit to support multiple processes associated with protocol deviation management from identification through reporting, including several feedback loops.

Digital clinical endpoints derived from wearables and other mobile and remote sensors hold great promise to improve clinical trial efficiency and patient outcomes. However, real issues regarding scientific validation, operational deployment, regulatory oversight and patient engagement and compliance pose significant challenges that must be overcome. In addition, real-world evidence (RWE) from remote sensors can be combined with other data streams in the health ecosystem (EMR, lab, claims, genomic data) to help improve patient care and clinical trial efficiency and effectiveness. What can we learn from what has been done to date and how do we position ourselves to accelerate the adoption of these tools in the future?

This panel will discuss the impact of technology and the role of biomarker and genetic on rare disease research and the pharma business model in general, as well as what industry can learn from a national advocacy org and its patient community. Panelists will discuss the following:

•The level of biomarker and genetic data collected adds more focus to Rare Diseases and Personalized Medicine, so how are companies focusing on internal infrastructures and engaging in more partnerships and collaborations to expand the pipelines?

•What new technologies and platforms are out there to target, engage and retain Rare Disease patients and communities?

•What can we learn from a national advocacy organization with ties to researchers, caregivers, patients and industry?