Tom Parke Implementing Adaptive Clinical Trials 4: Drug Supply

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?

1.How to make and supply many treatment arms 2.How much to make? 3.How much to supply?

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?

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.

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

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

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

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),...

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’.

Maximum required tablets per 100 subjects randomized Scheme 1Scheme 2 DoseMinMaxMinMax / Total285245

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

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

1.How to make and supply many treatment arms 2.How much to make? 3.How much to supply?

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

Example simulation: fitted curve Fitted response over progressive weeks

Example simulation: adaptive dose allocation

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

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.

How many do have to be able to supply?

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?

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%

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

Adding Doses At Interim Response Initial Doses

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?

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.

Simulate the supply Model: Centers Packs Subjects Randomization Shipments Depots

Simulate the supply

Simulate the supply

Simulate the supply

Simulate the supply

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

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

Chart the results of the simulations

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

Distribution of Required Supply

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

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?

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. Start of trial Interim End of trial Manufacturing time Initial Supply Final Supply

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

1.How to make and supply many treatment arms 2.How much to make? 3.How much to supply?

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

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?

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

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.

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

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!

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

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