Novel Trial Designs for Early Phase Drug Development

1. Novel Trial Designs for Early Phase Drug Development Elizabeth Garrett-Mayer, PhD Associate Professor Director of Biostatistics Hollings Cancer Center Medical University of South Carolina

2. Phase I trial goals Classic Phase I trials: Find the highest dose that is deemed safe: the Maximum Tolerated Dose (MTD) DLT = dose limiting toxicity Goal is to find the highest dose that has a DLT rate of x% or less (usually ranges from 20% to 40%) Newer Phase I trials: Find the dose that is considered to safe and have optimal biologic/immunologic effect (OBD). Goal is to optimize �biomarker� response within safety constraints.

3. Schematic of Classic Phase I Trial

4. Based on Presumption: Efficacy and toxicity both increase with dose

5. Classic Phase I approach: Algorithmic Designs �3+3� or �3 by 3� Prespecify a set of doses to consider, usually between 3 and 10 doses. MTD is considered highest dose at which 1 or 0 out of six patients experiences DLT. Confidence in MTD is usually poor.

6. Some properties of the �3+3� What can you learn from 3 patients at a single dose? What is the 95% exact c.i. for the probability of toxicity at a given dose if you observe 0/3 toxicities at that dose? ( 0, 0.64) 1/3 toxicities at that dose? (0.09, 0.91) 2/3 toxicities at that dose? (0.29, 0.99) 3/3 toxicities at that dose? (0.36, 1.00)

8. �Novel� Phase I approaches Continual reassessment method (CRM) (O�Quigley et al., Biometrics 1990) Many changes and updates in 20 years Tends to be most preferred by statisticians Other Bayesian designs (e.g. EWOC) and model-based designs (Cheng et al., JCO, 2004, v 22) Other improvements in algorithmic designs Accelerated titration design (Simon et al. 1999, JNCI) Up-down design (Storer, 1989, Biometrics)

9. CRM: Bayesian Adaptive Design Dose for next patient is determined based on toxicity responses of patients previously treated in the trial After each cohort of patients, posterior distribution is updated to give model prediction of optimal dose for a given level of toxicity (DLT rate) Find dose that is most consistent with desired DLT rate Modifications have been both Bayesian and non-Bayesian.

12. CRM Designs Underlying mathematical model Doses can be continuous or discrete Compared to the �3+3� the CRM is safer: fewer patients treated at toxic doses more accurate: selected MTD is closer to the true MTD more efficient: more patients are treated at doses near the MTD. Disadvantages: requires intensive involvement of statistician because future doses depend on model prediction need more lead time: statisticians need time (weeks?) to select the appropriate CRM design for a given trial simulations need to ensure that it will �behave� in a smart way

13. Long-term toxicities? CRMs and algorithmic designs take a long time to accrue, even with rapid accrual. Investigators may be interested in toxicities over a span of one to two years. For a study with only 15 patients with two year follow-up, �three-at-a-time� designs require 10 years to complete, even with perfect accrual. Need alternatives! Example scenario interested in the MTD as the 20%-tile of a toxicity requires 2 years followup (so we now have cohorts of 5, not 3).

14. Prorated Designs (Cheung & Chappell, 2000, Biometrics) Instead of collecting data on a group of 5 patients for 2 years each, Collect data on more than 5 patients for a total of 10 patient-years. One patient measured for one year counts (is �prorated� as) 1/2 of a patient. A Bayesian version (TIme-To-Event Continual Reassment Method, TITE-CRM, is available). Require more patients than traditional designs, provide more information at study�s conclusion; and Are much quicker than traditional designs (commensurate with the number of extra patients).

15. TITE-CRM: Schematic Example

16. Accelerated Titration Design (Simon et al., 1999, JNCI) The main distinguishing features (1) a rapid initial escalation phase (2) intra-patient dose escalation (3) analysis of results using a dose-toxicity model that incorporates info regarding toxicity and cumulative toxicity. �Design 4:� Begin with single patient cohorts, double dose steps (i.e., 100% increment) per dose level. When the first DLT is observed or the second instance of moderate toxicity is observed (in any course), the cohort for the current dose level is expanded to three patients At that point, the trial reverts to use of the standard phase 1 design for further cohorts. dose steps are now 40% increments.

17. Accelerated Titration Design �Rapid intrapatient dose escalation � in order to reduce the number of undertreated patients [in the trials themselves] and provide a substantial increase in the information obtained.� If a first dose does not induce toxicity, a patient may be escalated to a higher subsequent dose. Obviously requires toxicities to be acute. If they are, trial can be shortened.

18. Accelerated Titration Design After MTD is determined, a final �confirmatory� cohort is treated at a fixed dose. Jordan, et al. (2003) studied intrapatient escalation of carboplatin in ovarian cancer patients and found �The median MTD documented here using intrapatient dose escalation ... is remarkably similar to that derived from conventional phase I studies.� I.e., accelerated titration seems to work. Also, since it gives an MTD for each patient, it provides an idea about how MTDs vary between patients.

19. New paradigm: Targeted Therapy How do targeted therapies change the early phase drug development paradigm? Not all targeted therapies have toxicity Toxicity may not occur at all Toxicity may not increase with dose Targeted therapies may not reach the target of interest

20. Implications for Study Design Previous assumption may not hold Does efficacy increase with dose? Endpoint may no longer be appropriate Should we be looking for the MTD? What good is phase I if the agent does not hit the target?

21. Possible Dose-Toxicity & Dose-Efficacy Relationships for Targeted Agent

22. Trinary outcome CRM Y = 0 if no toxicity, no efficacy = 1 if no toxicity, efficacy = 2 if toxicity

23. Adding in a pre-phase I level? Phase 0 trials �Human micro-dosing� First in man Not dose finding Proof-of-principle Give small dose not expected to be therapeutic Test that target is modified Small N (10-15?) Short term: one dose Requires pre and post patient sampling. Usually PD assay. Provides useful info for phase I (or if you should simply abandon agent).

24. Phase 0: Example Parp-inhibitor ABT-888 administered as a single oral dose of 10, 25, or 50 mg Goals: determine dose range and time course over which ABT-888 inhibits PARP activity in tumor samples in PBMCs To evaluate ABT-888 pharmacokinetics Blood samples and tumor biopsies obtained pre- and postdrug for evaluation of PARP activity and PK If patients available, trials are quick. Exploratory Investigational New Drug (EIND)

25. Study Schema

26. Phase 0: Example Parp-inhibitor N = 13 patients with advanced malignancies N = 9 had paired tumor biopsies

27. Clin Cancer Res June 15, 2008 14 Designing Phase 0 Cancer Clinical Trials Oncologic Phase 0 Trials Incorporating Clinical Pharmacodynamics: from Concept to Patient A Phase 0 Trial of Riluzole in Patients with Resectable Stage III and IV Melanoma Preclinical Modeling of a Phase 0 Clinical Trial: Qualification of a Pharmacodynamic Assay of Poly (ADP-Ribose) Polymerase in Tumor Biopsies of Mouse Xenografts Phase 0 Trials: An Industry Perspective The Ethics of Phase 0 Oncology Trials Patient Perspectives on Phase 0 Clinical Trials The Development of Phase I Cancer Trial Methodologies: the Use of Pharmacokinetic and Pharmacodynamic End Points Sets the Scene for Phase 0 Cancer Clinical Trials Phase 0 Trials: Are They Ethically Challenged?

28. Article Coming out March 15 In Clinical Cancer Research Approaches to Phase 1 Clinical Trial Design Focused on Safety, Efficiency, and Selected Patient Populations: A Report from the Clinical Trial Design Task Force of the National Cancer Institute Investigational Drug Steering Committee. S. Percy Ivy, Lillian L. Siu, Elizabeth Garrett-Mayer, and Larry Rubinstein

29. Questions and Comments? garrettm@musc.edu