Clinical Trial Drug Supply Predictions Making Them Realistic and Near Real-Time
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Clinical trial drug supply requirements depend on when Patient Visits occur, and CMOs and CDMOs are criticized for not providing the drug supplies on time and budget. However, they struggle to improve their performance because those who plan and oversee the clinical trials don’t have a satisfactory way to indicate when Patient Visits will occur.
As a result, the CMOs and CDMOs cannot efficiently sequence their manufacturing campaigns and follow-on steps, resulting in waste levels that can reach 80%.
In this webinar, Steve Glan will show you that more accurate clinical trial drug supply predictions are possible, even when things go wrong during trial execution.
Steve has spent the last decade making clinical trial design and execution predictable, efficient, and transparent by applying his knowledge of engineering, manufacturing, clinical operations, math, and software development to large sets of clinical trial data.
By the end of the webinar, you will see the explicit connection between Patient Visits and clinical trial drug supply, improving planning between CROs/Pharma ClinOps and CMOs/CDMOas.
Who should attend?
Anyone who wants to understand a clinical trial from a process perspective, CMOs/CDMOs, CROs, and ClinOps personnel.
- Skill Level: Novice, Intermediate , Expert
1. Understanding What A Clinical Trial Is
1.1 Air Traffic Control Analogy
1.2 Process View of a Clinical Trial
2 Patient Visits: Connecting the Clinical Trial to Clinical Drug Supply
2.1 Starting at the End: On-Site Drug Supply Needs
2.2 Backing Up: Drug at Depots, Drug Shipped, Drug Packaged/Released, Drug Manufactured
3. The Path Forward: Integrating Clinical Operations and Drug Supply
Steve GalenOwner at Galen F P Solutions
Steve has a Ph.D. in Engineering and more than 27 years of experience in Big Pharma and global CROs. He has run the CRO division of a life sciences company and held senior leadership positions in clinical operations, software development, risk-based monitoring, clinical data science, project management, drug development, and pharmaceutical manufacturing, including clinical trial drug supply. Steve has spent the last decade making clinical trial design and execution predictable, efficient, and transparent by applying his knowledge of engineering, manufacturing, clinical operations, math, and software development to large sets of clinical trial data. Steve asserts that a clinical trial is not an AI/ML “black box” but rather a set of characterizable and connected processes that predictably produces data, like a manufacturing line. Steve is keenly aware that ROI matters when designing and running trials. His approach makes it easy to determine ROI before committing to a course of action intended to improve trial performance.