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

2 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

3 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)

4 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-chairs Presentation authors

5 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 6 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 7 Value of IRS in Adaptive Trials

8 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.

9 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

10 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 GSK Pfizer 11

12 Example: 4 week dosing to prevent thrombosis after hip replacement (GlaxoSmithKline) 1 tablet from each bottle, twice per day Bottle ABottle BBottle CBottle D Placebo 125 mg BIDPlacebo Odiparcil 250 mg BIDPlacebo Odiparcil 375 mg BIDPlaceboOdiparcil 500 mg BIDOdiparcil

13 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

14 Migraine example cont’d

15 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

16 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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19 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) Planned into code generation: … 5600 … 5611 ??? 20

21 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 22

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. 23

24 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% [( )/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. 25

Adaptive Design  The design was constructed by simulating the statistical operating characteristics using three likely dose response curves 26

Adaptive Design Simulation: Randomization Output File from Scenario C 27

28 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 29 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 30 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. 31 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. 32 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. 33 Weighing Increased Costs Against Benefits

34 Financial Implications Example: 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

35 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