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HCC Journal Club September 2009 Statistical Topic: Phase I studies

HCC Journal Club September 2009 Statistical Topic: Phase I studies. Selected article: Fong, Boss, Yap, Tutt, Wu, et al. Inhibition of Poly(ADP-Ribose) Polymerase in Tumors from BRCA Mutation Carriers The New England Journal of Medicine July 9, 2009. Vol. 361, No. 2, pp. 123-134.

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HCC Journal Club September 2009 Statistical Topic: Phase I studies

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  1. HCC Journal ClubSeptember 2009Statistical Topic: Phase I studies Selected article: Fong, Boss, Yap, Tutt, Wu, et al. Inhibition of Poly(ADP-Ribose) Polymerase in Tumors from BRCA Mutation Carriers The New England Journal of Medicine July 9, 2009. Vol. 361, No. 2, pp. 123-134

  2. Phase I studies • What are the goals? • Dose-finding • Safety • PK and PD • How are they designed? • What is the rationale for dose-selection?

  3. Fong et al. • Stated goals: Determine the following— • Safety • adverse-event profile • dose-limiting toxicity • maximum tolerated dose (MTD) • Dose at which PARP is maximally inhibited • PK profile • PD profile

  4. Study Design • “modified accelerated titration” • Not at all! • TRUE Accelerated titration design: • Treat 1 person per dose until either • one DLT is observed • OR, two grade 2 toxicities • Then, treat 3 patients per dose level • Dose steps can be doubling or not. • Fong study: • uses standard 3+3. • probably called modified AT because allows doubling of dose in the absence of grade 2 or higher. • Is NOT accelerated titration in the spirit of the original paper

  5. Back to basics: Acceptable toxicity • What is acceptable rate of toxicity? • 20%? • 30%? • 50%? • What is toxicity???? • Standard in cancer: Grade 4 hematologic or grade 3/4 non-hematologic toxicity • Always? • Does it depend on reversibility of toxicity? • Does it depend on intensity of treatment? • Tamoxifen? • Chemotherapy?

  6. ‘3+3’ Design • “Standard” Phase I trials (in oncology) use what is often called the ‘3+3’ design (aka ‘modified Fibonacci’): • Maximum tolerated dose (MTD) is considered highest dose at which 1 or 0 out of six patients experiences DLT. • Doses need to be pre-specified • Confidence in MTD is usually poor. • Treat 3 patients at dose K • If 0 patients experience dose-limiting toxicity (DLT), escalate to dose K+1 • If 2 or more patients experience DLT, de-escalate to level K-1 • If 1 patient experiences DLT, treat 3 more patients at dose level K • If 1 of 6 experiences DLT, escalate to dose level K+1 • If 2 or more of 6 experiences DLT, de-escalate to level K-1

  7. Observed Data in Fong Study

  8. Observed Data: with 95% confidence intervals

  9. This is actually better than most • Most studies treat only 3 or 6 at each dose level • With 0 of 6 DLTs: • Estimated DLT rate = 0% • 95% CI for DLT rate = [0%, 45%] • With 1 of 6 DLTs: • Estimated DLT rate = 17% • 95% CI for DLT rate = [0%, 64%]

  10. Why do we use it all the time? • It is terribly imprecise and inaccurate in its estimate of the MTD • Why? • MTD is not based on all of the data • Algorithm-based method • Ignores rate of toxicity!!! • Likely outcomes: • Choose a dose that is too high • Find in phase II that agent is too toxic. • Abandon further investigation or go back to phase I • Choose a dose that is too low • Find in phase II that agent is ineffective • Abandon agent

  11. We could use smarter designs! • Phase I is the most critical phase of drug development! • What makes a good design? MTD situation: • Accurate selection of MTD • dose close to true MTD • dose has DLT rate close to the one specified • Relatively few patients in trial are exposed to toxic doses • What makes a good design? Non-toxic agent situation: • Accurate selection of dose (range) which hits target • Relatively few patients are treated • Relatively few patients are exposed to ineffective doses

  12. This trial • Is MTD relevant? • What is the goal? • Should we be looking for hitting the target? • Toxicity ~ Efficacy? • PK and PD data presented • Although argument made for MTD, PARP inhibition is relatively constant for the higher doses

  13. Novel Designs • Why not impose a statistical model? • What do we “know” that would help? • Monotonicity (often) • Desired level of DLT • Statistical models improve: • Prediction • Efficiency • Accelerated Titration: incorporates model (next slide) • Example: CRM (continual reassessment method) • Originally devised by O’Quigley, Pepe and Fisher (1990) • dose for next patient was determined based on toxicity responses of patients previously treated in the trial • Others out there (and variations of CRM)

  14. Another Accelerated Titration Feature: Model fit

  15. CRM example

  16. CRM software example

  17. How would CRM have worked in this study? • Would have accelerated quickly • Would have iterated at a few doses • May not have treated so many patients at MTD • Would likely have been a smaller study • Could have used PD data to help dose-finding.

  18. Discussion • Phase 0 trials • PK and PD • single dose • 6-10 patients • Goals: • Define dose range for Phase I • Improve chance of success in phase I and II • Better planning of phase I • New and exciting! • First in man, pre-Phase I • Messy though: • Phase 0 vs. Phase 1? • How will this change Phase 1 goals? • My humble opinion: the development of Phase 0 strongly suggests that Phase I paradigm needs to be reconsidered

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