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

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)

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.

Dose Response

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!

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.

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

Commonly Seen Designs  Up-down methods  Accelerated titration  Continual Reassessment Method (CRM)

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 1.If 0 patients experience dose-limiting toxicity (DLT), escalate to dose K+1 2.If 2 or more patients experience DLT, de-escalate to level K-1 3.If 1 patient experiences DLT, treat 3 more patients at dose level K A.If 1 of 6 experiences DLT, escalate to dose level K+1 B.If 2 or more of 6 experiences DLT, de-escalate to level K-1

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 … Log-scale

Dose Increments Dose stepEqually spaced Modified Fibonacci Log scale (100%) (67%) (50%) (40%) (29%) (33%) (33%)

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

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

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

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

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

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….

Basic Idea of CRM Model chosen by Mathew etal.

Many models to choose from….

 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) Modified CRM (Goodman, Zahurak, and Piantadosi, Statistics in Medicine, 1995)

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

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

Observe first cohort  0 out of 6 treated at 30 mg/m 2 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?” a = 0.74

Observe second cohort  3 out 4 treated at 45 mg/m 2 had DLTs  Combine with 1 st cohort information to update a: a = 0.38

Observe third cohort  5 out 6 treated at 35 mg/m 2 had DLTs  Combine with other cohort information to update a: a = -0.25

Observe fourth cohort  3 out 6 treated at 30 mg/m 2 had DLTs  Combine with other cohort information to update a: a = Here, they decided to stop and declare 30 mg/m 2 the MTD

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

Other possible modeling options

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

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