Implementing Adaptive Clinical Trials 4: Drug Supply

1. Implementing Adaptive Clinical Trials4: Drug Supply Tom Parke tom.parke@tessella.com

2. Overview • There are more treatment arms • How do we supply more doses? • Arms may be dropped / introduced or arms may become more / less likely to be allocated • We don’t know how much of each dose we will need make / package? • We don’t know which doses to ship?

3. How to make and supply many treatment arms How much to make? How much to supply?

4. More treatment arms • How to manufacture / deliver multiple treatments? • manufacture each one • use combinations • may need multiple placebos • how to ensure patient compliance? • How to limit overage from additional treatment packs types?

5. How many treatment arms? • 8 doses is probably enough • Often use less (4-6) • Might use more (but only if it was easy) • Might start with few doses and add ‘in between doses’ only where needed.

6. Examples • Stroke: 16 doses (IV) • Migraine: 6 doses • Other A: 3 dose combinations • Other B: 5 doses • Other C: 7 doses and 4 doses

7. Examples • IV with 3 concentrations – randomiser sent ‘recipe’ to centre • Blister pack with all doses (single shot) • Take a combination • take a blue pill and a red pill • Make all 4 doses

8. Combinations • Can try to make as few dose strengths as possible ... • Strengths: 0, 1, 3 & 4 – in combinations of 2 • Strengths: 0, 1, 9 & 81 – in combinations of 3 • Strengths: 12.5, 50, 150 – in combinations of 3

9. Combinations cont’d • But can be difficult to predict required quantity of each strength. • Possibly simpler is say: • Strengths: 0, 1, 2, 4, 6, ... using the 1 dose to make intermediate doses. (0,0), (1,0), (2,0), (2,1), (4,0), (4,1), ...

10. Combinations • Assume: • that 20% get placebo, • 20% the best dose, • 15% the next two best doses, • 10% the two after that and • 5% the two after that. • Consider min & max requirements for tablets for each treatment dose in turn being ‘best’.

11. Maximum required tablets per 100 subjects randomized

12. Result • Need to make 14% fewer tablets per 100 subjects with simpler – more strengths scheme 2. • 47% less overrage to supply combinations. • Your mileage may vary, but fewer tablet strengths may not mean less wastage

13. Just in time packaging • Capsules can easily be made different strengths • If they can be made quickly & to order it is easier: • to provide adaptive supply as randomisations change • to prepare new doses to add at interim • Need prior warning from DMC before dropping or adding treatment arms. • DMC need to know lead time for implementation. • DMC need to monitor accumulating data / information • predict interim decision • predict timing of decision

14. How to make and supply many treatment arms How much to make? How much to supply?

15. Example Trial • Phase 2 trial of a Neuropathic Pain compound. • 8 doses plus placebo • Taken daily for 6 weeks • Maximum of 250 subjects

16. Example simulation: fitted curve Fitted response over progressive weeks

17. Example simulation: adaptive dose allocation

18. How much of each dose? • How can we determine how much to manufacture / package? • When should we schedule new batches to be manufactured / packaged?

19. Simulate the adaptive trial • Use not just one scenario, but the range of plausible scenarios • A max for each treatment arm that covers 90% or 95% of cases should suffice • Allow more for Placebo • Propose the limit to the designers – allow them to include the limit in their simulations.

20. How many do have to be able to supply?

21. Can we reduce the variance • Look at placebo distribution • P(allocate to placebo) is fairly uniform • Length of whisker is just randomness of allocation. • Don’t block because ratios are 2 sig fig (need blocksize of 100) and changed every week. • How about partial blocking?

22. Partial Blocking Placebo: 25% Dose1: 6% Dose2: 9% Dose3: 15% Dose4: 26% Dose5: 13% Dose6: 6% 1 Placebo 1 Dose4 + 2 of: Dose1: 12% Dose2: 18% Dose3: 30% Dose4: 2% Dose5: 26% Dose6: 12%

23. Treatment arms dropped or introduced • Trial may not adapt smoothly but only at interims (1-4 a trial) • At interim arms may be introduced, or dropped • Explore intermediate doses in area of interest • Extend range • Drop ineffective doses

24. Adding Doses At Interim Response Initial Doses

25. Treatment arms dropped or introduced • Can we reduce manufacturing / packaging and overage for arms that are dropped? • Can we avoid unnecessary re-supply for expired batches? • Can we avoid manufacturing / packaging an arm that is not then introduced?

26. Randomisation • Will be central, no site based • Need to have all possible doses at each center • Amount of wastage at centres that don’t recruit at all will be all the greater.

27. Simulate the supply • Model: • Centers • Packs • Subjects • Randomization • Shipments • Depots

28. 0.08 0.78 0.21 0.43 Simulate the supply

29. 0.97 0.66 0.14 0.01 Simulate the supply

30. Simulate the supply 0.40 0.23 0.61 0.85

31. Simulate the supply 0.78 0.08 0.72 0.37

32. Simulating adaptive trials

33. Simulating adaptive trials • Need to manage pack types • Need to include the adaptive randomisation – use output of simulation of adaptive trial: • Run supply simulations with many different randomisations

34. Run Simulations before Trial • Run 1000 simulations of the entire trial, with no supply cap (2 seconds per simulation for example of 20 centers x 400 days) • Get distribution of: • trial length • number of lost subjects • packs shipped from central pharmacy • If number subject lost unacceptable, check re-supply parameters & re-simulate

35. Chart the results of the simulations

36. Check Total Required Supply • Use number of packs shipped from pharmacy to estimate required supply. • Check over different scenarios that effect pack usage (e.g. adaptive randomisation scenarios) • Check with simulation • Estimate likely overage from simulations

37. Distribution of Required Supply

38. Simulate what-if scenarios • Frequency of re-supply to a patient (pack sizes) • Adaptive vs non-adaptive • Manufacture/package all upfront or make and initial stock and subsequent batches • Effect of batch expiry

39. Using the simulations during the study • Simulate forwards • Do we have enough supply? • Do have supply we can spare to another study? • When do we need that next batch? • What if we add / remove centres from the trial?

40. Treatment arms dropped or introduced • Monitor trial data regularly and prime manufacturing / packaging • Extend treatments by using dose combination – adding 1 dose to 0,2,4,6. • Manufacture remainder after interim • Use predictive power report to start manufacturing before interim. Initial Supply Final Supply Interim Manufacturing time Start of trial End of trial

41. Randomisation – Reduce Wastage • Use site based randomisation first, then central randomisation • Supply just in time – monitor the presence of subjects in screening • Pre-randomise subjects during screening and supply only what is needed. • Monitor and model recruitment rates during the trial and auto adjust the re-supply rules accordingly • Close non-recruiting centers and re-allocate supply

42. How to make and supply many treatment arms How much to make? How much to supply?

43. Re-supply during the study • What shipments should be sent today? • Load current data • recruitment rates • shipments • location of available stock

44. Treatment arms become more / less likely to be allocated • How do we ensure there is enough stock at centres for arms that are becoming more likely to be allocated? • How do we ensure that we don’t over supply arms that are less likely to be allocated?

45. Adaptive re-supply algorithm • Re-supply using a Bayesian self-tuning scheme. • At each centre the stock required is based on: ·Current stock. ·Packs in transit to the centre. ·Subjects likely to be recruited. ·Recruited subjects requiring fresh supplies.. ·Drug in stock which is about to expire

46. Adaptive re-supply algorithm (2) • Calculate for a look ahead time plus the time required to re-supply the centre. • Use a maximum acceptable probability of subjects being lost on recruitment - supplies are dispatched if that (floor) level will be exceeded. • Shipment is sized to reduce the probability of loosing a subject to below a lower maximum acceptable probability (ceiling) level.

47. Re-supply report Current % randomization to different arms Current patient screening data Current supply state Adaptive re-supply Weekly report

48. The Wyeth Experience: • Working with Adaptive Partner, developed tool to monitor site inventories: • Tracked treatment inventories at site. • Provided predicted requirements based on 99% and 95% certainty of randomization revision. • Predictions only based on patients within 4 days of end of screening period to prevent calculating demand on dropped patients. • Provided “pick list” of supplies required by site to accommodate the updated codes. • Information was provided to Clinical Supplies one week prior to having codes loaded into IVRS. • NO FORCED TREATMENT ALLOCATION occurred in this study!

49. And the winner is ... • Additional supplies were manufactured, packaged and stored at regional warehouses to accommodate evolving supply demands • Overall cost of drug supply for this study: • Cost of adaptive design: $422,000 • Number of patient kits packaged:1440 • Cost of traditional design: $201,000 • Number of patient kits packaged: 686 • But, savings to Clinical for closing study 2 months earlier and 180 less patients? $1.5 million

50. Summary • Adaptive trials are a challenge to supply (but they’re worth it) • More doses, less certainty • Use better tools: • simulator • adaptive re-supply • monitoring of patients in screening