Adaptive Clinical Trials In the Real World Presentation to MBC 23 rd April 2008

The questions 3 categories: 1.Should we use adaptive clinical trials or not? 2.What’s the impact of using them? 3.How do we use them?

To use or not to use

Adaptive Clinical Trials – to use or not to use? 1.When is it most appropriate to run an adaptive clinical trial? 2.When in a drug's development is the most appropriate time to conduct an adaptive clinical trial? 3.What indications particularly lend themselves to the use of adaptive clinical trials? 4.What is the value proposition in the use of Adaptive Clinical Trials? 5.What's driving the increasing use of adaptive clinical trials? 6.What are the key benefits for utilizing an adaptive clinical trial design?

1. When is it most appropriate to run adaptive clinical trial? When you have a lot to learn about the drug and the disease in your target population You do not have the time or money to simply recruit enough subjects in a simple way to answer you questions And there are outcomes early enough in treatment to adapt to

2. When in a drug's development is the most appropriate time to conduct an adaptive clinical trial? Any phase where there is significant uncertainty over the drug behavior But Phase 1 is adaptive anyway (could use better methods and could look at efficacy as well as toxicity) Phase 2 (PoC) and Phase 2 (Dose Finding) In Phase 3 there are regulatory issues – classical (frequentist) but not Bayesian statistics? Need for safety data? Phase 4? A lot of scope – but less budget.

3. What indications particularly lend themselves to the use of adaptive clinical trials? Quick response (<25% of recruitment period) Range of doses available Subjects are expensive Don’t want to learn equally about every treatment regardless of outcome.

Migraine, dental pain, post-operative pain, neuropathic pain Stroke, Alzheimer's, Schizophrenia Diabetes, cholesterol lowering Cancer Orphan indications 3b. What indications lend themselves to Adaptive Clinical Trials?

3c. What indications don’t lend themselves to the use of adaptive clinical trials? Very long time to final response Very swift recruitment Population change over duration of trial Subjects are cheap Want to learn equally about all treatment arms

Save 25-30% over parallel group with interim for futility. Additional investment ~$500,000 but net saving of $1.5M (400 subject trial) due to early termination for futility Costs: Extra supplies$200,000 Additional design$100,000 Response collection$100,000 Adaptive algorithm$100, What is the value proposition of using Adaptive Clinical Trials?

If successful, better characterization of the efficacy and toxicity of the drug More data on the dose of interest Less risk of an inconclusive outcome Better model of drug effect and disease progression – more persuasive Faster/smarter overall development through better targeted trials 4b. What is the value proposition of using Adaptive Clinical Trials?

5. What's driving the increasing use of adaptive clinical trials? Level of failure in Phase 3 Need better information before Phase 3 Need better killing of ineffective compounds before Phase 3 Time spent in development Can we learn faster by combining phases in a cleverer trial? Phase 1 & 2a Phase 2a & 2b Phase 2b & 3

6. What are the key benefits of adaptive clinical trials? Better Ethics Fewer subjects allocated to ineffective or over-toxic treatment arms Fewer subjects used in studies that fail Better Science Can try more doses (Phase 1 & 2) Can try more doses (Phase 3?) Explore other dimensions – combinations, indications, sub-populations Better Business Swifter curtailment of failing compound Better information -> better decisions at the next phase

6b.The key benefits Better definition of trial goal Modeling of trial data: Borrow ‘strength’ from neighboring points Borrow ‘strength’ from other outcomes (biomarkers, longitudinal, prior data, etc.) Optimization of dose allocation: Put fewer subjects on treatment arms that are clearly not working Put more subjects on treatments arms that seem to be showing the desired target effect. Result: better characterization of the dose behavior

Tom Parke ©2007 Tessella Support Services plc Example Adaptive Trial

Impact

1.How do adaptive clinical trials impact the whole development program? 2.What are the principle disadvantages (difficulties and costs) one faces when utilizing this approach?

1. How do adaptive clinical trials impact the development program? More flexibility in design of whole program Trials used to have very predefined task Now – what are your questions, and lets design a trial to answer them as efficiently as possible Consider trial in whole development program What will follow, what will be in parallel, what is the right order to answer the questions Need to think about the next trial earlier and longer Need to integrate the development team

2. What are the difficulties and costs of implementing adaptive clinical trials? Longer Design Time Need to identify candidate trial Design less “off-the-shelf” Design needs interaction with clinical team Design needs simulation and optimization More Integrated Trial Management System Quick capture of key responses Frequent modification of randomization Drug Supply Need to be able to deliver more doses Need to be able to use central randomization

Implementation

1.Can we capture the response data quickly and reliably 2.Can we calculate and agree the adaptation quickly 3.How do we implement the adaptation? 4.How does one effectively manage the clinical drug supply chain in an adaptive trial? 5.How do we get all the stakeholders aligned so the trial is a success? 6.From a clinical operations perspective, what are the challenges in managing a complex trial that could have from 300 to 800 subjects at multiple sites in different global locations? 7.How do we go about deploying adaptive clinical trials? 8.How do you make it mainstream and industrialize the process?

Example Adaptive Trial Infrastructure Model IVRS Response Data Capture Drug Supply Management Data Monitoring Committee Relative % Randomization Treatment Pack Data Randomization List Response Data DMC Report Randomization EDC Lite

Adaptive Trial Infrastructure Model IVRS EDC Drug Supply Management Data Monitoring Committee Relative % Randomization Treatment Pack Data Randomiszation List Response Data DMC Report Randomization EDC Lite

IVRS IVRS need modification to allow adaptation: To be able to regularly replace the randomization list after interim to drop or add doses after model update to adjust relative proportion of randomization Randomize dynamically based on the currently available arms and/or proportions of randomization Randomize dynamically using a combined blocking and proportionate randomization

Partial Blocking Required Randomization is: Placebo: 25% Dose1: 6% Dose2: 9% Dose3: 15% Dose4: 26% Dose5: 13% Dose6: 6% Partially blocked Random is now: Dose1: 8% Dose2:12% Dose3:20% Dose4: 35% Dose5:17% Dose6: 8% Random Placebo Random Partial blocking of placebo ensures % allocated to placebo and consistent allocation to placebo through time.

IVRS treatment allocation IVRS if not loading a randomization list needs to be able to supply a treatment allocation list: Patient ID,Treatment Arm , , , ,1 From first patient first visit and weekly or fortnightly thereafter.

Adaptive Trial Infrastructure Model IVRS EDC Drug Supply Management Data Monitoring Committee Relative % Randomization Treatment Pack Data Randomiszation List Response Data DMC Report Randomization EDC Lite

EDC EDC needs to be able to extract key response data: Patient ID, Visit #, resp1, resp , 1, 6.3, , 2, 5.2, , 3, 5.0, , 1, 4.3, , 2, 4.6, , 1, 5.9, , 1, 6.5, 0 Within a 1-2 months of first patient first visit and weekly or fortnightly thereafter.

EDC-Lite If the main EDC cannot produce response data quickly, frequently and reliably A parallel EDC system can be used, just collecting headline response values (possibly just two values per patient visit) Can be made convenient to use EDC-Lite data can be replaced by main EDC data as it becomes available Forward EDC-Lite data to EDC to aid data checking

Faxes in from centres: Subject screened Subject eligible Subject mobile phone # Faxes back to centres: Subject randomised Subject response overdue Monitoring by study manager Web access Subjects phone in: for randomisation with response Subjects receive: text reminder EDC-Lite

Adaptive Trial Infrastructure Model IVRS EDC Drug Supply Management Data Monitoring Committee Relative % Randomization Treatment Pack Data Randomiszation List Response Data DMC Report Randomization EDC Lite

Drug Supply Initial negotiation with supply as to what is possible number of different doses, quantity of API, etc. before design Trial design simulations provide estimates of max number of subjects allocates to any one treatment arm Trial supply simulations allow manufacturing estimate to be fine tuned, and supply / logistics trade-offs to be explored

Drug supply during Unblinded supply representative included on supply implications of DMC report. supply proportionate to probability of randomization total supply requirements implied by predictive probabilities

Adaptive Trial Infrastructure Model IVRS EDC Drug Supply Management Data Monitoring Committee Relative % Randomization Treatment Pack Data Randomiszation List Response Data DMC Report Randomization EDC Lite

Data Monitoring A process change not infrastructure DMC include someone competent to check: correctness of the data supplied to the model, the design’s performance, the implementation of the adaptation (is the randomization adapting?) Phase 1 & 2 trial DMCs staffed internally unless external specialist required. Regular automated DMC report with 10 minute teleconferences to review. Small number of big review meetings. Timing flexible based on review of report

DMC report The current recommendation The data The model fit The decisions The likely outcome (predictive probability)

Example trial setup Phase 2 dose finding Designs by Berry Consultants Data weekly from EDC (in-house, 3rd party) Possibly supplemented by direct fax of key endpoint data New randomizations sent to IVRS (in-house or 3 rd party) DMC report Secure file transfer

Example Weekly Update System Weekly complete response data New randomization list, or randomization probabilities DMC report EDC IVRS Stats Fax SMS Reminders Trial monitoring website

Main Clinical Operations challenges The high level data is collected and sent to the adaptive ‘back box’ reliably, accurately and frequently Efficiently supplying in a changing world But, you will be able to monitor your trial better

Adaptive Trial Infrastructure Model IVRS EDC Drug Supply Management Data Monitoring Committee Relative % Randomization Treatment Pack Data Randomiszation List Response Data DMC Report Randomization EDC Lite

Adaptive Design Need good tools (R, S-Plus, WinBUGS, Matlab) and good statisticians to generate designs, or very customizable implementations of designs. R, S-Plus, WinBUGS, Matlab – are very statistician friendly and good tools for researching designs – but slow for running large numbers of simulations. Can code them up (C++ / Fortran) once proven. Berry Consultants with Tessella will be releasing customizable implementations of Berry Consultants designs later this year.

Why simulate designs? For some trial designs we can no longer simply prescribe our desired probability of a false positive (alpha) and or false negative (beta). Simulate with treatment arms no more efficacious than placebo Simulate with different arms (and different numbers of arms) being clinically effective But there are other properties of interest too: How likely is the best treatment arm to be chosen? How likely are we to stop early and will it be correctly or incorrectly? What if we are studying more than one endpoint? Or more than one compound?

Why simulate designs? (2) We have more to decide: Is it worth doing an adaptive design? Which of these adaptive designs is better? What is the impact of this protocol change (more visits, more treatment arms, longer follow-up, change of endpoint)? For this design what values should I choose as design parameters: required confidence of futility/success to stop early the earliest the trial is allowed to stop early frequency of looking at data thresholds for dropping arms, adding arms etc.

Simulation Functionality Black Box Set and validate design parameters and scenarios to simulate Orchestrate running simulations of all versions of the design over all scenarios Display, analyze and chart the results Execute Trial with selected design and parameters Centralize storage of designs and run simulations on a computing grid Compare designs with common design constraints and scenarios Server Simulation manager GUI Trial Execution

Deployment at the company level Decide on type of adaptive trials you can and want to run. Establish cross functional adaptive review team (clinical, biostats, supply, IT, trial operations) to review candidate trials and assist teams to Go Adaptive. Development teams should be responsible for all compounds in an indication, not a single compound or So they see benefit in early determination of futility Can learn across a a number of trials

Aligning the stakeholders Involve them early help them understand what adaptive clinical trials are and why the company wants to use them in identifying the problems and solving them Ensure personal objectives are aligned with running adaptive clinical trials Top down & bottom up

Development teams Don’t design and evaluate design in isolation Trials aren’t islands, or steps on a single path They are decision nodes in a complex tree of investigation – looking at different endpoints, populations, indications, combinations. The more you can learn each trial and the more quickly you can learn, the more efficient you decision making and overall development. Can use interim data to start/stop other branches in the development

Example Phase 1 A B Combined phase 2a/2b in A & B Operationally seamless phase 3 with best of A or B Second confirmatory trial Poc complete start 2 nd indication development Sufficient confidence in efficacy to start manufacturing API for rest of development Start planning 2 nd confirmatory trial Drop a dose and chose dose for 2 nd trial

Implementation 1.Can we capture the response data quickly and reliably 2.Can we calculate and agree the adaptation quickly 3.How do we implement the adaptation? 4.How does one effectively manage the clinical drug supply chain in an adaptive trial? 5.How do we get all the stakeholders aligned so the trial is a success? 6.From a clinical operations perspective, what are the challenges in managing a complex trial that could have from 300 to 800 subjects at multiple sites in different global locations? 7.How do we go about deploying adaptive clinical trials? 8.How do you make it mainstream and industrialize the process?

You are not on your own Tessella and Berry Consultants can help you do this. Berry Consultants: Designs & “Black Boxes” Tessella: Simulation framework for black boxes Systems and services to help execute the trial

Summary Despite their differences from normal trials, Adaptive Clinical Trials can be implemented They are becoming increasingly easy to implement as we learn the lessons from the early adopters and build tools to support them As we integrate them fully into the development process, the benefits of cost savings and quicker and better informed decisions will continue to grow as the development process is redesigned