Motivating TrialVidaza® in AML patients undergoing allotx
Activates genes for apoptosis Modifies phenotype of leukemic cells to
potentiate a GVL effectDose-toxicity profile of Vidaza® is unknownThe cumulative risk of toxicity from repeated
administrations is unknown
Goal: Jointly optimize (Dose, Schedule)
Optimizing Dose and Schedule(Braun, Thall, Nguyen, deLima, 2007)
“Schedule” is a predetermined number of courses and days of administration within each course“Dose” = dose per administration
- Example, starting at time 0 : s(1) = (0, 1, 2, 7, 8, 9) s(2) = ( s(1) , s(1) + 14 days ) = (0, 1, 2, 7, 8, 9, 14, 15, 16, 21, 22, 23), etc.
-The agent may be administered as frequently as desired to each patient, provided the (dose,schedule) pair is sufficiently SAFE
- A patient’s actual administration times in the trial may deviate from the scheduled times
One cycle of an agent is administered at a fixedsequence of successive times s1 < s2 < . . . < sk ,the patient rests, & this is repeated one or more
times.
How many cycles can be given “safely?”
_______________________________________________
0 s1 s2 s3 s4 s5 s6 s7 s8rest
Hazard of toxicity from one administration :
A simple 3-parameter piecewise linear model
Each dose has its own 3-parameter triangle for the one-administration hazard
t = time in days
h(t)
Cumulative hazard of toxicity from
multiple administrations at a given dose
t = time in days
H(t)
Cumulative hazard of toxicity at day 10
t = time in days
H(t)
Cumulative Hazard of Toxicity by t* e = time of study entrysj = time of jth administration after e
(t* | dose, schedule)} =
∫ [0, t*] j h{u - (e + sj) | dose} du
Pr(Toxicity by time t* | dose, schedule) = F(t* | d,s,) = 1 – e-(t* | dose, schedule)
replaces the usual Pr(toxicity | dose) used for binary outcomes, ignoring schedule
Dose P erAdministrationmg m2
Day
of A
dmin
istr
atio
n (
Sch
edul
e )
8 16 24
028
5684
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
Dose per Administration (mg/m2)
Treat 1st patient at the lowest (dose, schedule) pair
Based on current Time-to-Toxicity data, treat each patient at the best (dose, schedule) pair
Do not “skip” untried (dose, schedule) pairs
If no (dose, schedule) pair is acceptable
Stop the trial
Trial Conduct
Dose P erAdministrationmg m2
Day
of A
dmin
istr
atio
n (
Sch
edul
e )
8 16 24
028
5684
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
Dose per Administration (mg/m2)
Dose P erAdministrationmg m2
Day
of A
dmin
istr
atio
n (
Sch
edul
e )
8 16 24
028
5684
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
-----
Dose per Administration (mg/m2)
What Actually Happened in
The Vidaza® Trial
Since only 1 toxicity occurred in the first 27 patients, Dr. de Lima decided to add 4 higher
dose levels of Vidaza:
32, 40, 48, 56 mg/m2
After receiving IRB approval, the trial was re-started with 4x7 = 28 (dose,schedule)
combinations
16 new (dose, schedule)
pairs
Final Optimal CombinationFinal Optimal Combination
(40 mg/m(40 mg/m22 per administration, 3 cycles) per administration, 3 cycles)
A case where all (dose,schedule) combinations
are unacceptably toxic
After pat. #2 began treatment at (8 mg/m2, 2 cycles),
pat. # 1 treated at (8 mg/m2, 1 cycle) experienced toxicity
All (dose,schedule) pairs are too toxic The Trial is Stopped
EarlyT
Dose-schedule toxicity cop
Hazard of toxicity from one administration
b
a = area
c
Final Data Analysis
Posterior mean and standard deviation (SD) of
per-administration hazard parameters in the Bayesian model for Pr(toxicity| PAD, number of cycles).
Areaa
Days to Peak of Hazard
b
Days from Peak of Hazard to
Endc
Duration (Days)b + c
PAD Mean (SD) Mean (SD) Mean (SD) Mean (SD)
8 0.0058 (0.0034)
14.5 (24.1) 8.7 (12.8) 23.2 (27.8)
16 0.0095 (0.0041)
14.9 (22.9) 14.4 (21.6) 29.4 (31.5)
24 0.0138 (0.0049)
11.7 (25.7) 20.3 (38.9) 32.0 (47.3)
32 0.0163 (0.0054)
15.9 (12.4) 31.3 (29.4) 47.3 (26.6)
40 0.0295 (0.0160)
14.0 (11.9) 32.0 (29.0) 46.0 (26.5)
Final Data Analysis
Posterior mean ptox = probability of toxicity by day 116
Per Administration Dose of Vidaza (mg/m2)
Number of Cycles 8 16 24 32 40 48 56
4 0.105 0.164 0.225 0.260 0.407 0.475 0.531
3 0.082 0.129 0.180 0.211 0.339 0.394 0.445
2 0.056 0.089 0.125 0.148 0.246 0.289 0.331
1 0.029 0.046 0.065 0.077 0.134 0.160 0.186
For ptox = Pr(toxicity within 116 days), each entry is the
posterior value of Prob( ptox > 0.30 ). For each combination of
(Number of Cycles, Per-Administration Dose),
A = acceptable toxicity, T = unacceptable toxicity.
Final Data Analysis
Per Administration Dose of Vidaza (mg/m2)
Number of Cycles
8 16 24 32 40 48 56
40.006
A0.034
A0.148
A0.269
A0.783
A0.920
T0.970
T
30.001
A0.005
A0.030
A0.083
A0.558
A0.755
A0.883
T
20.000
A0.000
A0.001
A0.004
A0.220
A0.371
A0.541
A
10.000
A0.000
A0.000
A0.000
A0.025
A0.045
A0.076
A
Take-Away Messages
1) The optimal combination
(3 cycles, 32 mg/m2 per day)
would not have been found using ANY other phase I methods.
2) Current work is to incorporate progression-free survival time, to be used along with time-to-toxicity, into the method.
Conclusions
The Dose-Schedule AlgorithmDose-Schedule Algorithm reliably
1) Finds (Dose,Schedule) pairs having specified Pr(Toxicity by day t)
2) Stops if no (Dose,Schedule) is acceptable
Implementation is Hard Work, but a free computer program “Dose Schedule Finder” is available from
http://biostatistics.mdanderson.org/SoftwareDownload