Bill Mietlowski, Biometrics and Data Management,
Novartis Oncology
KOL Adaptive Design seminar
July 8, 2011

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 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

 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
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Phase I Trial Challenges
Untested drug in resistant patients
Design Requirements
Escalating dose cohorts (3-6 patients)
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Primary objective: determine MTD Accurately estimate MTD
High toxicity potential:
Most responses occur 80%-120% of
MTD *
Find best dose for dose expansion
Complete trial in safety first timely fashion
Robustly avoid toxic doses
(“ overdosing ”)
Avoid subtherapeutic doses while controlling overdosing
Enroll more patients at acceptable**, active doses ( flexible cohort sizes )
Use available information efficiently
* Joffe and Miller 2006 JCO
** acceptable: less than or equal to the MTD determined on study

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 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 1 st cohort) as well as long-run simulations

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 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

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 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)

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 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)

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 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

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 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

Model-based designs have advantages over algorithmic designs
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
Two main approaches
• Algorithmic: fixed “data-only rules”, e.g. “3+3”
• Model-based: statistical
 accounts for uncertainty of true DLT rates
Applicability
Flexibility
Extendability
Algorithmic
Easy
Not very flexible
 fixed cohort size
 fixed doses
Rather difficult
Inference for true DLT rates
Observed DLT rates
Statistical requirements None only
Model-based
More complex due to statistical component (
 training)
Flexible: allows for
 different cohort sizes
 intermediate doses
Easily extendable

2 or more treatment arms
 combinations
Full inference, uncertainty assessed for true DLT rates
“reasonable” model, “good” statistics

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 =1/6
Go to next higher untested dose level or declare MTD otherwise
DLT =1/3
Enroll 3 additional pts at the same dose level
DLT >1/3
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
Go to next lower dose level or declare MTD at next lower dose level if 6 pts already tested
(never re-escalate)
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 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)


Are model-based designs too aggressive?

Example: Muler et al. (JCO 2004)
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• 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!

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 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.

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Typical priors represent different types of information

Bivariate normal prior for (log(

), log (

) )
 prior for DLT rates p
1
,p
2
,…
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

Historical
Data
(prior info)
Decisions
Dose Escalation
Decision
Trial Data
0/3,0/3,1/3,...
DLT rates p
1
, p
2
,...,p
MTD
,...
(uncertainty!)
Dose recommendations
Clinical
Expertise
Model based dose-DLT relationship
Responsible: Statistician
Informing: Clinician (Prior, DLT)
Responsible: Investigators/Clinician
Informing: Statistician (risk)
Model Inference Decision/Policy

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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.

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Practical and logistical aspects
Historical
Data
(prior info)
Trial Data
0/3@1 mg
Additional study data
(e.g. AE, labs, EKG,
PK, BM, Imaging
DLT rates p
1
, p
2
,...,p
MTD
,...
(uncertainty!)
Dose recommendations
Model based dose-DLT relationship
Decisions
Dose Escalation
Decision
Clinical
Expertise
Protocol development

Incorporating prior information
 Model Specification
Review design performance
Study conduct

Pts enrollment

Observation during each dose cohort
Preparation for the dose escalation conference (DETC)
Discussion/decision at the dose escalation conference (DETC)

(1) 22

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

(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

(3)
 Stopping rules (“rules for declaring the MTD”)
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• 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.)

(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

Patient enrollment / observation for each dose cohort
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
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

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
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

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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

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 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.

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
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

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
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


Many thanks to my Novartis Oncology BDM colleagues
• Beat Neuenschwander
• Stuart Bailey
• Jyotirmoy Dey
• Kannan Natarajan
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
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.


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