Tom Parke [email protected] Implementing Adaptive Clinical Trials 4: Drug Supply.

Tom Parke

[email protected]

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 1 Scheme 2

Dose Min Max Min Max

0 55 85 75 90

1 20 65 40 50

2 / 3 35 60 15 35

4 25 75 20 35

6 10 35

Total 285 245

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

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 Placebo1 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 InterimR

espo

nse

Initial Doses


• 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 Central Pharmacy

Depot 1 Depot 2

Center 1 Center 4 Center 3 Center 2

P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

0.78 0.430.080.21

Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

Simulate the supply

0.66 0.010.970.14

Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

Simulate the supply

0.23 0.850.400.61

Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

Simulate the supply

0.08 0.370.780.72

Simulating adaptive trials Central Pharmacy

Depot 1 Depot 2


P(R1)=0.05 P(R4)=0.02 P(R3)=0.04 P(R2)=0.02

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

Probability of losing subject

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8 >8

Subjects lost

Pro

bab

ilit

y

Re-supply A

Re-supply B

Re-supply C

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

Number of High Dose Packs shipped over 100 simulations

0

5

10

15

20

25

30

35

370 380 390 400 410 420 430 440 450 460 470

Number of packs

% o

f ti

mes

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?


• 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 trialManufacturing 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

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

Weeklyreport

Adaptivere-supply

Current supply state

Current % randomizationto different arms

Currentpatientscreeningdata

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

Tom Parke [email protected] Implementing Adaptive Clinical Trials 4: Drug Supply.

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Transcript of Tom Parke [email protected] Implementing Adaptive Clinical Trials 4: Drug Supply.