Utilizing Study Specific IRT Data for Advanced Clinical Supply Forecasting. Matthew Sargent Manager, Clinical Pharmacy CTM Operations

Transcription

1 Utilizing Study Specific IRT Data for Advanced Clinical Supply Forecasting Matthew Sargent Manager, Clinical Pharmacy CTM Operations

2 Disclaimer The presenter is a paid employee of Astellas. The positions taken and the opinions expressed in this presentation are those of the speaker and do not necessarily represent those of Astellas.

3 Agenda Current CTM forecasting challenges Problems with Today s Forecasting Standard Project End Goal Road map to implementation for first study Integration Considerations Expected Forecasting Improvements 3

4 Current CTM Forecasting Challenges Certain therapeutic areas (for example oncology, rare infectious disease, neglected diseases) of clinical trials make subject enrollment and forecasting difficult to predict Adaptive designs are complex to manage and may require five times as much clinical supply Lean commercial supply can be severely impacted by large clinical demand for comparators comparators are becoming harder to source Expensive comparators comprise significant portion of clinical study cost and have a fixed shelf-life R&D cost reduction initiatives places increased pressure to minimize clinical supply waste or overage 4

5 Problems with Today s Forecasting Standard Forecasts are created before FSI due to long manufacturing times with limited information Reforecasting is required based on the progression of the trial to solidify supply strategy For accelerated trials, some forecasts are made before first in man results are available for a compound Forecasts created are only as strong as the assumptions made Clinical Trial Material manufacturing plans are often determined based off of the initial forecast 5

6 The Forecast is complete On to packaging and resupply execution, right? Assumptions used prior to study start can be inaccurate Study teams often find themselves in a feast or famine situation: 1. CTM supply is expiring on the shelf due to: A. Decreased patient treatment duration B. Slow study enrollment C. Study start-up delays 2. CTM is in short supply due to: A. Patient treatment duration is longer than forecasted B. Accelerated study enrollment C. Changing study footprint (new countries/regions) 6

7 Re-forecasting is Needed, but Study Resource Time is Limited After the study begins enrolling, the team is focused on study drug packaging and resupplies Pulling patient enrollment, treatment assignment & treatment duration data from IRT is time consuming Manual data entry into forecasting system is time consuming with high risk of data entry errors. 7

8 Common IRT Integrations Sponsor Inventory System Vendor Hosted IRT Well established integration, allows for reactive planning as CTM inventory dwindles Often still requires separate reforecasting Allows for automatic re-forecasting based on updated enrollment and inventory figures Sponsor Forecasting System 8

9 Project Goals 1. Efficiency - Solution of incorporating forecast and real-time patient enrollment data effectively. This provides a more comprehensive view of CTM supply chain in one snapshot. 2.Visibility Re-plan forecasting reports are available and exportable. Data available for better decision making as it relates to CTM supply needs. 9

10 IRT + Forecasting Integration Points Integration files for the following data points were identified as most critical Patient Data Depot Inventory Data 10

11 IRT Interface Patient Data (Example File) Data that must exist in Forecasting before loading file: Trial ID, Cohort ID, Treatment ID, Country Forecasting Subject ID and Site are stored, but not utilized by the Enrollment module. Start Month determines the month that the subject will be used for first enrollment in the trial. Subjects IRT

12 IRT Interface Depot Inventory Data (Example file) Data that must exist in Forecasting before loading file: Item ID Forecasting Orderable Lots IRT

13 IRT + Forecasting Integration Considerations Involve your IRT vendor and Forecast System vendor early on. Create a requirements document to be agreed upon by all three parties (IRT+ Forecasting +Sponsor) Create item/kit names in your forecasting system during initial forecast that will be used in your IRT kit set up Follow standard Software development life cycle (e.g. URS, Development, UAT & Go-Live) Automate the data transfer: Current use of XML file uploaded to an FTP server and automatically downloaded to the forecasting system. Determine your integration frequency upfront for each study 13

14 Forecasting Expectations & Benefits Category Future Forecasting Expectations Reduced Overage Results in reduced manufacturing campaigns and associated waste Accuracy Attrition Efficiency Reduced Waste Incorporating real-time results from patient data and modified duration of treatment expectations for 2 nd forecasts -Use of real-time information for future planned studies of same compound Use of real time attrition rates to adjust future manufacturing and packaging campaigns With new IRT real-life data sets available, re-forecasting will become a less time consuming task Tighter more frequent forecasting/reforecasting to reduce CTM waste and cost 14

15 Questions? Contact Information: Phone:

16 Acknowledgements Yuki Hayashi, CTM Manager, Astellas Japan Melissa Gallardo, Sr. Clinical Supplies Associate, Astellas US Viki Vestuto, BRS Consultant, Astellas 16