Often, the unexpected does happen that may cause unforeseen logistical challenges. Expecting the unexpected and planning appropriately ensures reliable and timely delivery of clinical samples.
Getting a drug to market used to be relatively simple and straightforward. That’s just not the case anymore. Clinical trials from first-in-human to post-submission face new challenges every day. Whether it’s about a treatment for a chronic ambulatory condition, precision medicine or cell and gene therapy, a massive uptick in complexity is observed.
Most Phase II and Phase III studies have multiple endpoints and procedures per patient generating millions of data points from a single study. And with the exponential rise in technology and capability, these numbers are only going to go up. Not only is this more burdensome for patients, and sites to recruit patients, but it increases the complexity of the logistics and sample handling for trials since samples comprise most data for any submission.
Therefore, the question raised is: How to ensure the labs working on these increasingly complex studies aren’t getting bogged down, and are they moving fast enough for the lives that depend on them?
Join this webinar to gain valuable insights into effective clinical sample management techniques and essential precautions to prevent sample loss. Attendees will explore the realm of sample handling, agile shipping strategies, digital technology for ensuring a robust chain of custody and the optimal solutions for maintaining the clinical sample integrity during storage.
Register for the live webinar taking place on Tuesday, October 3, 2023, at 11am EDT (4pm BST/UK) featuring expert speakers Stephanie Weber, Vice President, Sample Tracking, SampleGISTICS, LabConnect; Joe Marino, Senior Director, FSP Solutions, LabConnect; and Julia Tarasenko, Senior Vice President, Global Commercial Operations, Sales and Pricing, Marken.
For more information, or to register for this event, visit Clinical Sample Management: Expecting the Unexpected – How to Avoid Losing a Sample.
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