(a.k.a. Phase I trials)

1. (a.k.a. Phase I trials) Dose Finding Studies

2. Dose Finding • Dose finding trials: broad class of early development trial designs whose purpose is to find a dose of treatment that is optimal with respect to simple criteria • Toxicity • Efficacy • Low risk of side effects • Several dose related questions of interest in therapeutic development • Dose-efficacy association • Dose-safety association • Schedule-efficacy association • Interactions between therapies (i.e. combinations of treatments)

3. Dose Finding • Many possible dose optima • Minimum effective dose • Maximum non-toxic dose • Maximum tolerated dose • Ideal therapeutic dose (hard to control) • General dose-finding question is complex, but tendency has been to focus on dose-safety association • Utilize some basic assumptions about the dose-safety association.

4. Dose Response

5. Maximum Tolerated Dose (MTD) • The classic objective of dose finding in oncology: select the dose that yields a pre-specified frequency of toxicity. • Designs intended to be “dose titrations” of “optimizations” while allowing tolerable toxicity • Basic assumption of “more is better” leads to notion of “MTD” • Very prevalent approach, but obvious limitations with regard to the more general dose finding problem • With vaccine and other “non-toxic” treatments, MTD might not be an appropriate conceptualization of the desired outcome!

6. Idealized Dose Finding Design • Randomly assign subjects to one of a few doses. • Treat adequate number at each dose level • Fit plausible dose response model for interpolation • Get unbiased estimate of true dose-response probabilities.

7. Ideal Design Not Feasible • In human trials, we cannot randomize patients to high doses until lower ones have been explored. • Instead, ‘sequential’ or ‘adaptive’ designs • But, • we don’t want to treat many subjects at low, ineffective doses • We don’t want to use doses that are too high and produce frequent serious side effects

8. Commonly Seen Designs • Up-down methods • Accelerated titration • Continual Reassessment Method (CRM)

9. Up-Down Designs • Most common • “Standard” Phase I trials (in oncology) use what is often called the ‘3+3’ design • 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

10. Up-Down Design Considerations • Number of patients per dose level: most often 3, but can be any number of patients (usually between 1 and 6) • Choosing doses • Equally-spaced • (Modified) Fibonacci • “true” Fibonacci sequence is 1, 1, 2, 3, 5, 8, 13,… • “golden ratio” properties where ratio of successive numbers approaches 0.61803… • Log-scale

11. Dose Increments

12. Advantages of Classic Designs • Simplicity of design, execution, inference • Meet ethical needs of exploring low doses first • Provide simple, operational definition of the target dose • Considerable clinical experience and comfort with their use • They can be easily studied quantitatively and possibly improved

13. Classic Designs in Practice • Generally, not very accurate depiction of true dose-response (or dose-toxicity) curve Ideal stopping probabilities Operating characteristics of design True dose-toxicity probabilities

14. Additional Issues • Require dose levels specified in advance • Usually start far from target dose • Don’t fully use information from previously treated patients • Don’t use information on ordinal response (e.g. graded toxicity) • Estimate of MTD is seriously biased or invalid

15. Accelerated Titration • Similar to traditional design with small cohorts at low doses • Attempts to use information in ordinal toxicity responses at lower doses • May reduce the number of patients needed to reach MTD

16. Continual Reassessment Method • Allows statistical modeling of optimal dose: dose-response relationship is assumed to behave in a certain way • Can be based on “safety” or “efficacy” outcome (or both). • Design searches for best dose given a desired toxicity or efficacy level and does so in an efficient way. • This design REALLY requires a statistician throughout the trial. • Advantage is increased efficiency and precision, low bias compared to non-model-based methods • Disadvantage is sophistication

17. CRM history in brief • Originally devised by O’Quigley, Pepe and Fisher (1990) where dose for next patient was determined based on responses of patients previously treated in the trial • Due to safety concerns, several authors developed variants • Modified CRM (Goodman et al. 1995) • Extended CRM [2 stage] (Moller, 1995) • Restricted CRM (Moller, 1995) • and others….

18. Basic Idea of CRM Model chosen by Mathew etal.

19. Many models to choose from….

20. Modified CRM (Goodman, Zahurak, and Piantadosi, Statistics in Medicine, 1995) • Carry-overs from standard CRM • Mathematical dose-toxicity model must be assumed • To do this, need to think about the dose-response curve and get preliminary model. • More common to use a “logit” model • We CHOOSE the level of toxicity that we desire for the MTD (p = 0.30) • At end of trial, we can estimate dose response curve. • ‘prior distribution’ (mathematical subtlety)

21. Modified CRM by Goodman, Zahurak, and Piantadosi(Statistics in Medicine, 1995) • Modifications by Goodman et al. • Use ‘standard’ dose escalation model until first toxicity is observed: • Choose cohort sizes of 1, 2, or 3 • Use standard ‘3+3’ design (or, in this case, ‘2+2’) • Upon first toxicity, fit the dose-response model using observed data • Estimate a • Find dose that is closest to toxicity of 0.3. • Does not allow escalation to increase by more than one dose level. • De-escalation can occur by more than one dose level. • Dose levels are discrete: need to round to closest level

22. Starting the CRM • Assume a=0 to start • Want dose with DLT rate of 30% a = 0

23. Observe first cohort a = 0.74 • 0 out of 6 treated at 30 mg/m2 had DLT • Use statistical model to find best estimate of a based on updated information: • ”What value of a is most consistent with data, given our model?”

24. Observe second cohort • 3 out 4 treated at 45 mg/m2 had DLTs • Combine with 1st cohort information to update a: a = 0.38

25. Observe third cohort • 5 out 6 treated at 35 mg/m2 had DLTs • Combine with other cohort information to update a: a = -0.25

26. Observe fourth cohort • 3 out 6 treated at 30 mg/m2 had DLTs • Combine with other cohort information to update a: a = -0.34 Here, they decided to stop and declare 30 mg/m2 the MTD

27. Why did they stop? • Not completely clear • Looks like their model did not adequately describe toxicities • Too flat • Perhaps a more flexible model (2 parameter) would have been better

28. Other possible modeling options

29. Pros and Cons of CRM • Advantages • Estimation method is not biased • Does not depend strongly on starting dose • Efficient for finding target dose • Encapsulates subjectivity of dose-finding designs • Can use ordinal information • Can incorporate PK data in dose escalation • Disadvantages/Criticisms • If model choice is not flexible, might not escalate and estimate efficiently • Clinicians do not like complexity • Some worry about treating patients at high dose levels

30. Summary • Oncology dose finding tends to be very narrowly focused methodology • Classic dosing studies are deficient for dose finding • CRM is method of choice for finding optimal dose • Application of CRM can be improved by careful planning • Extensions exist for CRM and classic designs for “dual dose” finding