Transcript MTD - Biopharmnet.com

Phase I dose escalation studies in Oncology:
a call for on-study safety and flexibility
Bill Mietlowski, Biometrics and Data Management,
Novartis Oncology
KOL Adaptive Design seminar
July 8, 2011
Outline of Presentation
2
 Challenges of Phase I setting in Oncology
 Design requirements
 Proposed designs: algorithmic (e.g. 3+3) and continual
reassessment method (CRM) vs. design requirements
 Novartis Oncology standard: Bayesian logistic regression with
escalation for overdose control to determine potentially unsafe
doses
 Protocols and dose escalation teleconferences to choose among
the potentially safe doses
 Conclusions
Dose escalation setting in Oncology
 Primary objective: Estimate maximum tolerable dose
(MTD) based on acceptable rate of dose-limiting toxicities
(DLT)
 Assume true DLT rate at MTD is in (0.16, 0.33)
 Generally small number of patients resistant/refractory to other
therapies : often 15 to 30
 Adaptive setting: dose escalations depend on DLT data
 One dose (often MTD) usually selected for dose expansion
 Large uncertainty during and at the end of the trial
3
Challenges and Design Requirements for
Oncology Phase I Trials
4
Phase I Trial Challenges
Design Requirements
Untested drug in resistant patients
Escalating dose cohorts (3-6 patients)
Primary objective: determine MTD
Accurately estimate MTD
High toxicity potential: safety first
Robustly avoid toxic doses
(“overdosing”)
Most responses occur 80%-120% of
MTD *
Avoid subtherapeutic doses while
controlling overdosing
Find best dose for dose expansion
Enroll more patients at acceptable**,
active doses (flexible cohort sizes)
Complete trial in timely fashion
Use available information efficiently
* Joffe and Miller 2006 JCO
** acceptable: less than or equal to the MTD determined on study
MTD Targeting and Safety
5
 Statisticians have taken great care to show operating
characteristics of designs under different dose response
shapes (steep, shallow, etc.)
 Show likelihood of finding true MTD, underdosing, overdosing, etc.
 However, published on-study safety characteristics very important
to clinicians and regulators
 Number of patients exposed to excessively toxic doses in actual
trials a concern
 Need to do extensive data scenario testing (performance of
model under explicit occurrences, e.g. x DLTs in 3 patients at 1st
cohort) as well as long-run simulations
Heterogeneity in Cancer Trials
6
 There is often substantial heterogeneity in cancer trials
 Rogatko et al (2004) show patient characteristics can compete
with dose with regard to adverse events.
 There can be marked treatment x marker interaction in terms of
efficacy (e.g. cetuximab and panitumumab in KRAS wild-type vs.
KRAS mutated colorectal cancer) (Amado et al (2008))
 Predictive biomarker may require early diagnostic development
Impact of Dose Chosen for Expansion
7
 Dose selected for dose expansion generally becomes the
recommended phase II dose (RP2D)
 If MTD underestimated, so is RP2D.
 If MTD overestimated, RP2D may be overestimated and MTD must be
re-estimated if toxicity issues emerge
 May choose dose lower than cycle 1 MTD as RP2D based on available
clinical data
 Carefully choose the RP2D during dose escalation
 May need to enrich at safe and active doses near MTD (flexible
cohort sizes)
Flexible cohort sizes may be useful when:
8
 PK is erratic, dose proportionality is questionable
 > linear or < linear
 High potential for chronic (long term) toxicity
 Need ample evaluable patients for later cycles at dose cohort
 Enrich to understand degree of activity
 More patients in Phase II population
 More patients with tumor samples
 If predictive biomarker is a concern (e.g. need n=8 patients in a
cohort to have 90% likelihood of at least 1 marker + and at least
1 marker – patient if prob (marker +) =0.25)
Efficient use of available information – prior
9
 Prior DLT information from previous Phase I studies may
be available for
 New Phase I study for that agent
 New Phase Ib combination trial
 Prior information about DLTs from one schedule may be
available for new schedule of the same agent
 Proposed DE design should efficiently use available
prior information
Efficient use of available information – emerging 10
 Sometimes, multiple schedules or both single agents and
combos are studied in parallel (but perhaps staggered) in
the same DE trial
 Should exploit structural information if possible
 DLTs on MWF schedule  Increased likelihood of DLT for daily
dosing at the same dose
 DLTs on single agent  Increased likelihood of DLT for
combination at the same single agent dose
 Proposed DE design should efficiently use this
emerging information
Approaches/Designs
11
Model-based designs have advantages over algorithmic designs
 Two main approaches
•
•
Algorithmic: fixed “data-only rules”, e.g. “3+3”
Model-based: statistical  accounts for uncertainty of true DLT rates
Algorithmic
Model-based
Applicability
Easy
More complex due to statistical
component ( training)
Flexibility
Not very flexible
 fixed cohort size
 fixed doses
Flexible: allows for
 different cohort sizes
 intermediate doses
Extendability
Rather difficult
Easily extendable
 2 or more treatment arms
 combinations
Inference for
true DLT rates
Observed DLT
rates only
Full inference, uncertainty assessed for
true DLT rates
Statistical requirements
None
“reasonable” model, “good” statistics
Traditional 3+3 design
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New cohort at a new dose
level: Enroll 3 patients
DLT =0/3
Go to next higher dose level
or same dose if highest dose
level
DLT =1/3
DLT >1/3
Enroll 3 additional pts
at the same dose level
Go to next lower dose level
or declare MTD at next lower
dose level if 6 pts already tested
(never re-escalate)
DLT =1/6
DLT >1/6
Go to next higher untested
dose level or
declare MTD otherwise
Go to next lower dose level
or declare MTD at next lower
dose level if 6 pts already tested
(never re-escalate)
Published performance of 3+3 design
13
 Low probability of selecting true MTD (e.g. Thall and Lee. 2003)
 High variability in MTD estimates (Goodman et al. 1995)
 Poor targeting of MTD on study:
• Low MTD: Can assign toxic doses to relatively large number of
patients (Rogatko et al. 2007)
• High MTD: Tends to declare MTD at dose levels below the true MTD
• Behavior depends on number of cohorts before MTD – too many leads
to underdosing, too few leads to overdosing (Chen et al. 2009)
Alternative approach needed to meet
Oncology study design requirements
Case Report with Model Based Design
 Are model-based designs too aggressive?
 Example: Muler et al. (JCO 2004)
• Continual Reassessment Method (CRM)
• One-parameter model was used.
• MTD recommendation from CRM: 50mg!
- Indeed an aggressive recommendation.
- Poor model fit and ignores uncertainty about DLT rate
• Is it justified? No!
14
CRM analysis for Muler et al
15
Our standard dose escalation design
16
 Bayesian logistic regression with escalation with overdose
control (EWOC) (since 2004) (Neuenschwander et al 2008
SIM)
 Three key intervals:
• Underdosing → Pr (true DLT rate < 0.16)
• Targeted toxicity → Pr (true DLT rate is in (0.16, 0.33))
• Overdosing→ Pr (true DLT rate >0.33)
 EWOC criteria mandates that posterior probability of
overdosing <0.25.
BLR-EWOC applied to Muler et al data
17
Priors
18
Typical priors represent different types of information
 Bivariate normal prior for (log(),log())  prior for DLT rates p1,p2,…
Uninformative Prior
• wide 95%-intervals
• (default prior)
Historical Prior
• Data from historical trials
(discounted due to
between-trial variation!)
Mixture Prior
• Different prior information
(pre-clinical variation)
• different prior weights
Clinically driven, statistically supported decisions
Historical
Data
Decisions
(prior info)
Dose Escalation
Trial Data
0/3,0/3,1/3,...
DLT rates
p1, p2,...,pMTD,...
(uncertainty!)
Dose
recommendations
Clinical
Model based
dose-DLT
relationship
Expertise
Responsible: Statistician
Informing: Clinician (Prior, DLT)
Model
Decision
Inference
Responsible: Investigators/Clinician
Informing: Statistician (risk)
Decision/Policy
Summary of statistical component
20
Model
Prior
Expertise
Input

Inference

Recommendations
1. Substantial uncertainty in MTD finding requires statistical component
2. Input: standard model (logistic regression) + prior
3. Inference: probabilistic quantification of DLT rates, a requirement that
leads to informed recommendations/decisions
4. Dose Recommendations are based on the probability of
-
targeted toxicity
and overdosing. Overdose criterion is essential.
Combination of clinical and statistical expertise
21
Practical and logistical aspects
Historical
Data
(prior info)
Additional
study data
(e.g. AE, labs, EKG,
PK, BM, Imaging
Trial Data
0/3@1 mg
DLT rates
p1, p2,...,pMTD,...
(uncertainty!)
Model based
dose-DLT
relationship
Dose
recommendations
Decisions
Dose Escalation
Decision
Clinical
Expertise
Protocol development
Study conduct
 Incorporating prior
information
 Model Specification
Review design
performance
 Pts enrollment
 Observation
during each
dose cohort
Preparation for the
dose escalation
conference (DETC)
Discussion/decision
at the dose
escalation
conference (DETC)
Protocol development (1)
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 Model Specification - Incorporating prior information
• Preclinical toxicity data (with possible difference among
species/gender),
• STD10 and/or HNSTD translated to human doses and
respective start doses
• Shape of dose-toxicity relationship – variations as singleagent
• Previous clinical trials
• Literature data related to compounds, combination partners,
etc.
• Relevance of study population
Protocol development (2)
23
 Design Specification
• Pre-define provisional dose escalation steps
-
Provisional doses decided on expected escalation scheme - typically
indicate maximum one-step jump. Intermediate doses may be used
on data-driven basis
• Minimum cohort-size – typically 3.
-
Allow enrollment of additional subjects for dropouts or cohort
expansion
• Pre-define DLT criteria and appropriate toxicity intervals
• Pre-define evaluable patients for DLT assessment
-
All patients with DLT are included
-
For patients with no DLT, they must have sufficient drug exposure
and completed required safety assessment to be sure of “no” DLT, or
they are excluded
Protocol development (3)
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 Stopping rules (“rules for declaring the MTD”)
• At least x patients at the MTD level with at least y patients
evaluated in total in the dose escalation phase
or
• At least z patients evaluated at a dose level with a high precision
(model recommends the same dose as the highest dose that is
not an overdose with at least q% posterior probability in the target
toxicity interval.)
Protocol development (4)
25
 Statistician test-runs the design (if required)
• Decisions under various data scenarios (scenario testing)
-
e.g. what happens if we see 0, 1 or 2 DLT in the first, second or third
cohort?
-
or - what escalations can be made if we see no DLT in first 6
cohorts?
• Operating characteristics (simulation testing)
-
Performance of the design in terms of correct dose-determination,
gain in efficiency under various assumed dose-toxicity relationships
(truths)
 Clinicians review design performance document
• Appended to protocol for HA/IRB review
Study conduct
26
Patient enrollment / observation for each dose cohort
 To assure patient safety during the conduct of the study a
close interaction within clinical team is required
•
Clinician, statistician, clinical pharmacologist, etc
•
Investigators
 Clinical trial leader provides regular updates on accrual:
•
For each cohort enroll subjects per minimum cohort-size, typically 3
•
May enroll additional subjects up to a pre-specified maximum

In the case of unexpected or severe toxicity all investigators will
be informed immediately

The model will be updated in case the first 2 patients in a cohort
experience DLT
Dose escalation teleconference (DETC)
27

DETC scheduled close to all subjects in cohort being
“evaluable”

Statistician is informed how many DLT and evaluable subjects
are expected at the DETC

Statistician performs analysis with number of patients
with/without DLT from all cohorts

Prior to DETC key safety data, labs, VS, ECG, PK, PD, antitumor activity, particularly from current cohort as well as
previous cohorts are shared with investigators

Real time data for discussion – not necessarily audited
Dose escalation teleconference (DETC)
28
Discussion with investigators during the DETC
•
Investigators and sponsor review all available data (DLT, AE,
labs, VS, ECG, PK, PD, efficacy) particularly from current
cohort as well as previous cohorts
•
Agree on total number of DLTs and evaluable subjects for
current cohort
•
Statistician informs participants of the highest dose level one
may escalate to per statistical analysis and protocol
restrictions
Dose escalation decision
29
 Participants decide if synthesis of relevant clinical
data justifies a dose escalation and to which dose
(highest supported by the Bayesian analysis and
protocol or intermediate)
 Even though BLR-EWOC recommends dose
escalation, team may enroll more at current dose to
learn more from PK/PD, potential safety issues (later
toxicities, lower grade toxicities, etc.)
 Decisions are documented via minutes and
communicated to all participants.
Summary
30
 Patient safety is the primary objective
• Statistical approach quantifies knowledge about DLT data only
• Statistical inference is used as one component of a decision-making
framework
- Provides upper bound for potential doses based on uncertainty statements
- To reduce risk of overdose  obtain more information at lower doses
 Logistical application of our approach can be protocol/drug
specific
• Maximum escalation steps, minimum and maximum cohort sizes,
stopping rules are pre-specified
 Studies require active review of ongoing study data by Novartis
and investigators
Current state of Oncology Phase I trials
31
 Rogatko et al (2007)
• Investigated about 1200 Phase I Oncology trials
• Only about 1.6% used innovative designs (most used 3+3)
• In the past 3-4 years, the number has increased to 3-4%
 This is disappointing. Reasons are:
• Phase I has (for too long) been non-statistical
• 20 years of using the CRM has not changed this
• Large scale implementation of innovative (Bayesian ) designs
require a lot of effort
• Guidance / support from key stakeholders is needed
 Improper dose/regimen/patient population identified as a
leading cause of failure of Phase III trials
Acknowledgements
 Many thanks to my Novartis Oncology BDM colleagues
• Beat Neuenschwander
• Stuart Bailey
• Jyotirmoy Dey
• Kannan Natarajan
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References
 Amado, Wolf, Peeters, Van Cutsem et al (2008)
Wild Type KRAS is required for panitumumab efficacy in patients with metastaic
colorectal cancer Journal of Clinical Oncology, 26:1626-1634
 Babb, Rogatko, Zacks (1998).
Cancer Phase I clinical trials: efficient dose escalation with overdose control .
Statistics in Medicine, 17:1103-1120
 Bailey, Neuenschwander, Laird, Branson (2009).
A Bayesian case study in oncology phase I combination dose-finding using logistic
regression with covariates. Journal of Biopharmaceutical Statistics, 19:369-484
 Chen, Krailo, Sun, Azen (2009).
Range and trend of the expected toxicity level (ETL) in standard A+B designs: A
report from the children’s oncology group. Contemporary Clinical Trials, 30:123-128.
 Goodman,Zahurak, Piantadosi (1995).
Some practical improvements in the continual reassessment method for Phase I
studies. Statistics in Medicine, 14:1149-1161.
References
 Joffe, Miller (2006).
Rethinking risk-benefit assessment for Phase I cancer trials. Journal of Clinical
Oncology, 24:2987-2990
 Neuenschwander, Branson, Gsponer (2008)
Critical aspects of the Bayesian approach to Phase I cancer trials. Statistics in
Medicine, 27:2420-2439
 Rogatko, Babb, Wang, Slifker, Hudes (2004)
Patient characteristics compete with dose as predictors of acute treatment toxicity
in early phase clinical trials . Clinical Cancer Research 10: 4645-4651.
 Rogatko, Schroeneck, Jonas, Tighioart, Khuri, Porter (2007).
Translation of innovative designs into Phase I trials. Journal of Clinical Oncology,
25: 4982-4986.
 Thall, Lee (2003)
Practical model-based dose-finding in phase I clinical trials: methods based on
toxicity. Int J Gynecol Cancer 13: 251-261
 Thall, Millikan, Mueller, Lee (2003)
Dose-finding with two agents in phase I oncology trials. Biometrics 59:487-496