Drug Supply Management for Adaptive Trials

Micheline Marshall (Pfizer)Nancy Burnham (GlaxoSmithKline)

NitinPatel (Cytel Inc)

March 12, 2010

On behalf of the Adaptive Design Working Group - Mfg. & Drug Supply Work Stream

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Introduction Management of the drug supply is often more complicated than

traditional studies in adaptive clinical trials.

This is particularly true for those studies where modifications involve:

Number of trial subjects may be increased or decreased

Dosages may be dropped or added

Allocation to dose groups is modified

Complications are:

The amount of drug that will be required is unknown

May need to mask modifications to dose in order to minimize bias

Need to ensure availability of drug to sites at adaptive decision points

Efficiency of drug supply is necessary for the success of most adaptive trials

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Introduction, cont’d. Currently, most companies do not have systems, tools, and

processes designed to manage the unique demands of adaptive trials.

As a consequence, most companies either: Outsource all or part of trial operations to an expensive vendor Devise complicated work-around

Adaptive clinical trials are still new within the industry with many companies just becoming involved in this initative.

The PhRMA Adaptive Designs Working Group formed the Manufacturing & Clinical Trial Supply Substream to

Share experience across the industry Communicate good practice and guidance (manuscript near completion)

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Adaptive Design Working Group – Manufacturing and Clinical Trial Supplies Substream

Abbott: Ajay Shah, Dave Shah ClinPhone: Graham Nicholl Cytel: Judith Quinlan*, Nitin Patel Tessella: Tom Parke Lilly: LB Wong Merck: Weili He Pfizer: Micheline Marshall GlaxoSmithKline: Nancy Burnham*, Angelique Ditoro

*Co-chairsPresentation authors

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Randomization / Interactive Response System

Communication Strategy

Supply chain strategy

Simulation Tools

Finance

Key Points

Topics

IVRS/IWRS – Interactive Voice/Web Response System:

Automated centralized system

Allows sites to randomize subjects via the telephone or web

Assigns clinical supplies at each dispensing visit

Controls site and/or local depot clinical supplies inventory based on real time utilization

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Interactive Response Systems

Central tool to update changes to randomization:

Immediate links new randomization schedule to package numbers

Flexible and efficient inventory management to meet the needs of adaptive changes to treatment allocation

Robust audit trail to reflect modifications

Date / time stamps

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Value of IRS in Adaptive Trials

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Supply Chain Strategy:Cross Functional Collaboration

To achieve an effective drug supply outcome, it is important the entire project team discusses the following:

Identify the adaptive modifications in the protocol that would ultimately affect the supply demand.

Commit to timelines around decision.

Explore likely scenarios of dosing configurations as the study progresses.

Agree to how many capsules/tablets/injections are acceptable for the patient per dose.

Define the dose-strengths to be manufactured.

Agree on dosing strategy for ongoing subjects assigned to dropped doses.

Evaluate different packaging options to minimize overage, maintain flexibility and achieve appropriate level of masking.

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Clinical Supply Challenges Responding to decision points:

Are the right doses available at sites?

How are dose modifications communicated to supplies group?

When is adjusted inventory required at sites?

Has sufficient time been factored to complete inventory adjustments?

Changes to the randomization Updating the IRS databases Adjust inventories to sites

Factor in the “knowns”:

Min/max number of treatment types to be assigned

Min/max subjects per treatment regimen

Is the ratio of comparator subjects known

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Distribution Logistics A good strategy can cope with global distribution

as changes to treatment allocation evolve.

Number of countries

Number of depots

Number of sites

All will affect the dilution of supplies

Actual Study Examples

GSKPfizer

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Example: 4 week dosing to prevent thrombosis after hip replacement (GlaxoSmithKline)

1 tablet from each bottle, twice per day

Bottle A Bottle B Bottle C Bottle D

Placebo Placebo Placebo Placebo Placebo

125 mg BID Placebo Placebo Placebo Odiparcil

250 mg BID Placebo Placebo Odiparcil Odiparcil

375 mg BID Placebo Odiparcil Odiparcil Odiparcil

500 mg BID Odiparcil Odiparcil Odiparcil Odiparcil

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Example: Acute Treatment of Migraines (GSK with Tessella tech support)

Seven doses: 5, 15, 30, 60, 120, 180 mg Placebo

Subjects dosed at onset of migraine

Endpoint – response after 2 hours

Randomization updated after every patient response Continual reassessment method Increase chances patients receive best dose

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Migraine example cont’d

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Migraine Example, cont’d

3 pack types with dosages in different order

Subjects randomized to a pack type

Upon migraine onset subjects call IVRS and told which tablet to take

After 2 hours subject calls IVRS and keys in response

Randomization system updated for next subject

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Forecasting Tool ex: Pfizer (Wyeth) Diabetes Trial

Tessella custom developed tool:

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 provided to Clinical Supplies one week prior to having codes loaded into IVRS.

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Long Term Considerations

Process flows must be developed to detail the communication between all involved parties.

IT must be involved to ensure smooth integration between systems.

Blinding techniques must be developed to automatically mask pack code gaps due to adding new treatments mid study.

Build strategy to add new treatment arms mid-study without alerting the site personnel.

Develop an on-demand strategy to maximize supply flexibility and minimize supply overages.

Masking Pack Codes

Treatment Arm A (known and assigned at study start)

Treatment Arm B (known and assigned at study start)

Treatment Arm C (dose unknown and added mid study)

Treatment Arm D (dose unknown and never utilized)

1001

1002

1003

1004

1005

Planned into code generation:

1008

1006

1007

… 5600

… 5611

???

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Drug Supply Modeling and Simulation - 1 Computer simulation models of the supply chain

can provide reliable answers to important questions about drug supply at the planning stage

Examples: What quantities should be manufactured and packed? What is the effect of multiple manufacturing and packing

campaigns? What is the effect of adding more countries and sites? How much API can we save by combining pack types to

make up different dosages? 21

Drug Supply Modeling and Simulation - 2

Drug supply simulation models can also enable efficient operations during the conduct of a trial

Examples: Adjusting trigger levels and resupply quantities to reflect

actual enrollment rates at sites Predicting future enrollment at sites based on actual

enrollment to-date Predicting requirements for different pack types at sites

taking into account new enrollments as well as revisits and drop-out rates

Suggesting transfer of stocks from low recruiting sites to faster recruiting sites

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Leveraging statistical simulations in drug supply simulations Design of almost all adaptive trials requires running

simulations to determine the statistical operating characteristics for a set of likely scenarios. These simulations compute randomization information for each subject in the trial that reflects the adaptive aspects of the design. This randomization information is saved in a file for use by the drug supply simulation software.

Drug supply simulation software takes various inputs from the user that describe site accrual rates, dosages, pack-types and supply chain parameters. It then reads the files of randomization information created by the adaptive design simulations to compute the behavior of the drug supply chain for the adaptive design.

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Case Study: Estimating drug requirement at start of study

Bayesian Dose Adaptive Trial Design

Placebo-controlled, double-blinded, phase II dose-finding trial with 7 active doses. Sample size is 120 subjects.

Ten cohorts of 12 subjects each enrolled during the study, with first cohort having 4 subjects on placebo, 2 on dose 4 and 1 each on all other doses

Subject responses in each cohort are used to update

randomization ratios for active doses for the next cohort

Single packs for each dose (placebo or active dose), requiring 120 kits for 120 subjects

Overage Estimate (no simulation) Allocation of active doses in first cohort is known but for the

remaining nine cohorts it depends on the responses observed. A conservative approach is to provide 72 kits (worst case = 9 cohorts x 8 subjects per cohort) for each dose of the study drug. Total number of kits required is 552 (40+2+1x6+72x7); overage is 360% [(552-120)/120]

If we consider specifics of the adaptive design and likely scenarios considered in the design we can simulate the chances of extreme requirement outcomes and arrive at a better balance between risk of randomization failure (stock-out) and overage.

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Adaptive Design The design was constructed by simulating the statistical

operating characteristics using three likely dose response curves

0

5

10

15

20

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0 1 2 3 4 5 6 7Dose

Me

an

Re

sp

on

se

Flat Response:Scenario AMedium Response:Scenario BHigh Response: Scenario C

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Adaptive Design Simulation: Randomization Output File from Scenario C

Sim

#

Sub

ject

#

Coh

ort#

Dos

e

Res

pons

e

1 1 1 2 101 2 1 6 32.91 3 1 7 18.51 4 1 5 26.11 5 1 3 2.131 6 1 4 20.61 7 1 4 12.41 8 1 0 -8.51 9 1 0 -0.41 10 1 1 17.61 11 1 0 -5.71 12 1 0 0.611 13 2 1 7.991 14 2 0 9.061 15 2 0 201 16 2 1 9.75

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Overage Estimate using Simulation

Drug supply simulations were run using the

randomization files from design simulations for each of the three likely dose response curves.

Running 500 drug supply simulations showed that there would be no stock out under any scenario if 216 kits were prepared.

Thus the estimated requirement of 552 (360% overage) without simulations is reduced to 216 kits (80% overage) if a stock-out probability of less than 1/500 is acceptable

We can run more simulations if we wish to estimate drug requirement for a lower stock-out probability

Beyond the study level consider a financial comparison of development strategies

Consider financial implications of different strategies in a clinical development program in which adaptive designs are used instead of, or in addition to traditional designs

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Evaluating Financial Benefits

Drug supply costs for an adaptive design is apt to be higher due increased overage.

Randomization costs are also likely to be higher for designs that require frequent changes in randomization ratios.

Continual Reassessment Models may also require expensive outsourcing of randomization to a vendor with a more sophisticated system. E.g.: GSK migraine study

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Financial Considerations: Increased Costs

Overage is highest when the maximum for each dose is drug packaged in advance to accommodate every possible scenario at every site.

Overage can be reduced by an adaptive strategy that adjusts while the trial is ongoing to accommodate algorithmic modifications suggested by the accruing clinical data.

The best approach for a given trial will be driven by amount of drug available, the number of regions, and recruitment rate.

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Managing Increased Costs

Consider redistributing supplies from slow recruiting sites to fast recruiting sites, closing slow recruiting sites.

Consider the feasibility of slowing or suspending recruitment at decision points so that sites can be resupplied.

If at trial is long enough, there may be opportunity for multiple manufacturing and/or packaging campaigns to preserve material that isn’t needed.

If a trial allows an adaptive increase in sample size think about opening new centers. Look for opportunities to pool supplies across trials.

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Managing Increased Costs, cont’d

At the study level, increased drug supply costs maybe offset by the benefits of stopping the trial early and reduce the number of subjects and other operational costs

At the program level, increased drug supply costs maybe offset against improvements in development strategies

Combining objectives PoM and POC POC and dose ranging

Improved dose selection for phase III Reduced late phase attrition Lower down-stream costs and higher revenues.

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Weighing Increased Costs Against Benefits

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Financial ImplicationsExample: Wyeth Trial

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

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Key Points: Efficient trial supply for an adaptive clinical trial requires:

X-functional discussions

Modeling and Simulation

Supply Forecasting There’s no one-size fits all packaging and distribution

strategy: Useful tools: IVRS, forecasting tools

Flexibility in packaging

Imaginative thinking Potential greater cost of adaptive trial should be

considered in the context of benefit to overall asset development strategy