While the industry is taking baby steps into using machine learning in clinical trials and for other data-generating objectives, the value of this data will depend on the transparency of algorithms, robustness of data sources, and extrapolation to real-world outcomes.
While pursuing hid PhD in chemical engineering at MIT, Armon Sharei developed an interest in novel cell therapies. That interest led him to a discovery that today is the foundation of SQZ Biotech, a cell-therapy company where he serves as CEO. Sharei discovered a new method of inserting materials into cells more effectively than anything that currently existed.
Advancements in technology create new avenues for optimizing clinical trials and the lessons that can be learned from them. With the gap between advancements in technology and regulatory uptake in clinical trials, it is important to rigorously examine technology applications. While the promise of new technology can lead to overenthusiastic adoption, mirroring the pace of other industries may not always lead to the best outcomes for patients. To increase regulatory buy-in to the use of these technologies, the clinical trial world needs a thorough understanding of the functionalities of each technical innovation, its potential utility and limitations, and how to build trust among patients, regulators, and other healthcare professionals.
In his role as product line owner for digital health at Janssen, Mark Sapp leads a team that works directly with different therapeutic areas inside the company. “Our focus is helping our clinical teams get from point A to point B,” says Sapp. “We assist them with taking their idea and scaling it up, identifying the clinical endpoints they hope to achieve, and helping them stay focused on the results they need to generate.”
Patient-facing digital technologies — also referred to as patient technology (PT) — have the potential to make a significant impact on the clinical research environment. PT includes any digital technology patients interact with to participate in clinical trial activities. When implemented effectively, these technologies can enhance patients’ experiences and engagement, streamline clinical trial processes, and enable better, more robust data collection in clinical trials.
In March 2019 I had the opportunity to interview Jennifer Newman, Global Project Leader, Regulatory Affairs/Clinical Operations for Celldex Therapeutics. Newman was once part of the largest implementation of risk-based monitoring (RBM) and was able to share insights from her experience.
As part of our ongoing series detailing an approach for the rapid assessment and prototyping of digital and other clinical trial technologies, we introduce here the use of artificial intelligence (AI) and machine learning (ML) to optimize clinical study design and execution.
In March 2019 I had the opportunity to interview Jennifer Newman, Global Project Leader, Regulatory Affairs/Clinical Operations for Celldex Therapeutics. Newman was once part of the largest implementation of risk-based monitoring (RBM) and was able to share insights from her experience.
Idorsia Pharmaceuticals Ltd was founded in June 2017 and came about as a result of the acquisition of Actelion Pharmaceuticals by Johnson & Johnson. Transition agreements allowed the company to use legacy systems from the prior company for a period of 18 months. By the time the agreements expired, Idorsia would have to design its own systems landscape.
Three decades ago when I entered the profession of clinical research, our workplace equipment extended to typewriters, white-out, mimeographs, hand-written documents, rubber erasers, pens, label makers, and fax and copy machines. Rows of massive, locked, fire-proof filing cabinets storing millions of papers for a nationally funded research program lined record rooms, hallways, and every spare corner of the offices. Most of us felt rewarded when we could use a typewriter with a correction key despite one’s typing skills being firmly judged by the illegible mistakes in the carbon copies.
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