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The Elusive Promise of Neuroprotection - What have we learned, Where do we go next? by William Barsan
The document discusses the challenges and failures in neuroprotection research, particularly in stroke and traumatic brain injury, highlighting the inadequacies of traditional clinical trial designs and the need for adaptive trial methodologies. It emphasizes the importance of better collaboration in preclinical and clinical research, the redesign of trial processes, and utilizing flexible designs to improve outcomes. Additionally, it touches on the long journey to obtaining research funding, particularly for emergency medicine physicians, and the factors contributing to a successful research career.
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The Elusive Promise of Neuroprotection - What have we learned, Where do we go next? by William Barsan
- 1. The Elusive Promiseof Neuroprotection - What Have we Learned, Where do we go next? And…….How to develop a successful research career! William Barsan, MD
- 2. Neuroprotection • Can applyto any acquired neurological condition • Implies impeding the injury cascade by targeting one or multiple molecular pathways • Can apply to stroke, TBI, global ischemia, etc • Stroke and TBI have been the most studied areas
- 3. Failure of Neuroprotection •TBI – 20 late Phase 2 or Phase 3 trials – All have failed to show a benefit – More than 130 monotherapies look good in preclinical work • Ischemic stroke – 15 drugs have advanced to Phase 3 trial testing – All have failed
- 4. Failure of Neuroprotection •Multiple drug classes have been tested – Glutamate antagonists – Calcium channel blockers – Anti-inflammatories – Antioxidants – GABA antagonists – Opiate antagonists – Growth factors – Others
- 5. Clinical Trial failuresin Stroke • Drugs were tested too late after stroke onset • Inadequate sample size to detect a modest treatment effect • Studies included too many mild or severe stroke patients in whom detecting a treatment effect was difficult • Side effects precluded dosing to achieve adequate plasma drug concentrations • Wrong dose? • Wrong duration of treatment?
- 6. What is ourtypical model? • Discovery research into pathways, mechanisms • Preclinical animal models of disease • Phase 1 toxicity, PK testing • Phase 2 trials for safety, potential efficacy • Phase 3 trials
- 7. Steps in TraditionalDrug Development • Phase I – tens of subjects – first use in humans (with or without target illness) – generates initial dosing and toxicity information • Phase II – one to few hundreds of subjects with target illness – gain initial information on dose-response relationship (i.e., “proof of concept”), side effects • Phase III – confirm superiority of new treatment – need two “adequate and well controlled” trials
- 8. Challenges in TraditionalApproach • Phase 2 – a wide range of dose strengths may be possible “best” choices – may also need to consider combinations of treatments, different durations of treatment – may not really know the right patient population – Most Phase 2 studies are actually underpowered Phase 3 trials – No way of comparing different agents “successful” in Phase 2
- 9. Challenges in TraditionalApproach • Phase III – often still don’t really know the right dose or timing of treatment – don’t really know what to expect in the control arm – don’t know the right population – don’t know anything about rarer side effects • Yet traditional statistical approaches require that the trial characteristics be completely defined prior to enrolling the first phase III patient
- 10. Is the Modelbroken?? • Preclinical testing – Typically done in single labs with individual models – Can’t compare agents from different trials – Only evaluate one agent – Often not blinded or randomized – Outdated statistical methodology – Models may not be clinically relevant – Outcomes are not clinically relevant
- 11. Is the Modelbroken?? • Phase 2 trials – Inadequate data from preclinical trials to know what is the most promising agent(s) – Evaluate only one agent at a time – Outcomes may not be clinically relevant – Study designs not comparable for different agents – Often use “best guess” for dosage, duration, time of treatment – Underpowered for efficacy – Unable to predict success in Phase 3
- 12. Is the Modelbroken?? • Phase 3 – Evaluating one agent at a time – How do we know we’re evaluating the right agent? – Use of “fixed” trial designs – Often “guess” at dose, duration, timing – Wrong patient population – Very high failure rate
- 13. Is the Modelbroken?? • Errors are compounded by our outmoded process • Choosing the wrong agents to test • Failure to compare multiple agents • Failure to identify right dose, duration, timing • Failure to identify target patient population • A failed trial may be a failed design, not a failed treatment
- 14. Evaluation of combinationagents • Current mechanism is to do one at a time • Prove efficacy of one agent alone before evaluating in combination • It is not logical that a blocking a single pathway will lead to improved outcome when multiple injury pathways are active • Examples from cancer treatment are instructive • Use of adaptive trial designs
- 15. “Philosophy” of Adaptive Trials •Clarity of goals – E.g., proof of concept vs. identification of dose to carry forward vs. confirmation of benefit – A statistically significant p value is not a goal • Frequent “looks” at the data and data- driven modification of the trial • Adaptive “by design” • Extensive use of simulation to “fine tune” key trial characteristics
- 16. Traditional vs. FlexibleMethods Component Traditional Flexible Interim Analyses Limited (1 to 2) Frequent Randomization Fixed (1:1, 2:1) Variable Number of Arms Limited (2 to 3) Few to Many Use of Incomplete Data Imputation at Final Analysis Imputation at All Stages Philosophy Frequentist Bayesian or Frequentist Control of Error Rates Via Theoretical Calculation Via Extensive Simulation
- 17. Adaptation: Definition • Makingplanned, well-defined changes in key clinical trial design parameters, during trial execution based on data from that trial, to achieve goals of validity, scientific efficiency, and safety – Planned: Possible adaptations defined a priori – Well-defined: Criteria for adapting defined – Key parameters: Not minor inclusion or exclusion criteria, routine amendments, etc. – Validity: Reliable statistical inference
- 18. The Adaptive Process Analyze AvailableData Continue Data Collection Begin Data Collection with Initial Allocation and Sampling Rules Stopping Rule Met? Stop Trial or Begin Next Phase in Seamless Design Revise Allocation and Sampling Rules per Adaptive Algorithm No Yes
- 19. Why Do AdaptiveClinical Trials? • To avoid getting the wrong answer! – Drawing an incorrect qualitative conclusion • To avoid taking too long to draw the right conclusion – Time, human subjects, and resources
- 21. Avoiding Anticipated Regret •A substantial fraction of all confirmatory trials fail despite promising “learn phase” results • Investigators can anticipate the design decisions they would wish to “take over” after the trial fails • Areas of “anticipated regret” are promising targets for adaptations
- 22. One Truly UnderstandsOnly What One Can Simulate • The relative importance and likelihood of threats to trial validity depends on the specifics of the situation • Simulation is the best way to quantify the different threats to validity and inform rational trial design
- 23. • Frequent interimanalyses • Explicit longitudinal modeling of the relationship between proximate endpoints and the primary endpoint of the trial • Response-adaptive randomization to efficiently address one or more trial goals • Explicit decision rules based on predictive probabilities at each interim analysis • Dose-response modeling • Extensive simulations of trial performance Some Adaptive Strategies
- 24. Example Outcome ofFixed ACDRS, 9/11/2012 24 • Idealized Outcome? • Answer All your questions? • Do anything differently?
- 25. Example Outcome ofFixed ACDRS, 9/11/2012 25 Perfect Outcome Later Effect Earlier Effect
- 26. Example Outcome ofFixed ACDRS, 9/11/2012 26 Null Harm Null/Harm • Another Study? • FDA Approval? • Medical Controversy ?
- 27. 27 Observed Rate— though delayedhere Modeled Rate
- 28.
- 29. Future research inNeuroprotection • Redesign of preclinical research • Redesign of the Phase 2 process • Redesign of the Phase 3 process
- 30. Preclinical research • Muchcloser collaboration with clinical researchers – Clinical researchers need to help with design • Use of multicenter preclinical trial networks – Blinding, randomization, data analysis • Development of “ongoing” trials with different experimental models • Use of Adaptive Designs • Test and compare individual agents and combinations of agents • Better dose and duration finding
- 32. Phase 2 process •Informed by more adequate preclinical designs • Use of Adaptive Designs • Perform “Platform” Phase 2 studies • Add in different agents or combinations • Weed out the losers to avoid further waste • Develop decision rules for proceeding to Phase 3 • Better definition of dose and duration
- 33. Phase 3 trials •Adaptive designs • Can add exploratory arms with hierarchical modeling • Frequent interim analyses • Enrichment to define optimal population • Use of longitudinal modeling • Use of response adaptive randomization • Avoid “missing” something that works!
- 34. Future of Neuroprotection •Failed translation has created sense of nihilism regarding neuroprotection • Our preclinical and clinical trial testing have significant shortcomings • Need to develop better screening processes to identify promising agents & combinations • Redesigning our approach utilizing flexible trial designs • Consider “re-testing” failed agents using this new approach
- 35. And the nextquestion is:
- 36. How do youdevelop a successful research career?
- 37. The Long Roadto Independent Funding Over the past 4 decades, the time from theOver the past 4 decades, the time from the completion of training to awarding of one’scompletion of training to awarding of one’s first R01 has lengthened significantly.first R01 has lengthened significantly. The time from completion of residency toThe time from completion of residency to receipt of this funding is on average longerreceipt of this funding is on average longer than the time spent in medical school andthan the time spent in medical school and residencyresidency This hasn’t escaped the NIH’s noticeThis hasn’t escaped the NIH’s notice Recent changes to ranking system to promoteRecent changes to ranking system to promote first time investigatorsfirst time investigators New changes to shorten the turn around ofNew changes to shorten the turn around of applications for first time investigatorsapplications for first time investigators Van Epps, Younger: “Early Career Productivity among Emergency Medicine Physicians with RO1 Grant funding”, Acad Emerg Med 18:759-762, 2011
- 38. Challenges Arising • Forthe Chair – Very long lead time from investment to pay off – Early stars may fade – Fraction of faculty ‘going for it’ is small and potentially expensive – Many EM Chairs have not gone down this road personally, so expectations may be partly (mostly?) guesswork • For the faculty member – Very long lead time from investment to pay off – May become relatively isolated from other faculty and departmental activities – Life outside of work will be threatened by the workload – Often must look outside of EM for best mentorship
- 39. The Annual PerformanceEvaluation: So, How’s it Going?
- 40. How Should ItBe Going? • R01 recipients in Emergency Medicine sought by polling the Association of Academic Chairs of Emergency Medicine • CVs collected to determine, in the window from end of residency to awarding of R01: – Time to award – Publication rate per year prior to R01 funding – Site of publications – Other grants pursued as PI and as Co-I Van Epps, Younger: “Early Career Productivity among Emergency Medicine Physicians with RO1 Grant funding”, Acad Emerg Med 18:759-762, 2011
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- 42. Time from residencyto RO1 award
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- 44. Life-Work Balance DuringRun-Up Feature Mean (IQR) Clinical Effort, as % of Appointment 50 (25, 60) Years of Marriage 9 (3, 14) Child-Years 8 (0, 15)
- 45. Summary of FacultyStudy • Faculty (and their bosses) working towards an R01 should anticipate a decade of effort following residency – More time spent working towards that grant than in medical school and residency combined • Fellowships reduced the time to funding by ~ 5 years – Compare to the two years invested… • Consistent publication and small grant writing were the norm • Life went on – Most faculty had spouses, kids for the majority of the residency-to-R01 interval
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- 47. How did Iget involved in clinical neurological research? • I never did a neurology rotation—ever! • I had no interest in stroke when I began doing research • I have never taken a statistics course • I started as a laboratory researcher
- 48. My first articlein 1979
- 49. First 10 yearsout of residency • 1979-1984 – Lab research (cardiac arrest, shock) • 1982 – First Clinical trial (industry funded) • 1984 – NIH: Naloxone in Acute Stroke (co-I) – Pharma—Ancrod in acute stroke (co-I) • 1986 – NIH: tPA pilot studies (first patient treated in 1987—co-PI) • 1988 – NIH: Ultra early evaluation of ICH (co-I) • 1990 – NIH: NINDS tPA randomized trial (co-PI)
- 50. What went rightfor me in developing a research career? • Goals – Always interested in academics – Research--a way to leverage knowledge • Collaboration – Inside and outside of EM • Mentors • Pursuing opportunities – Being in the right place at the right time can be a conscious decision • Perseverance
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