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 3

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

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 12

New cohort at a new dose level: Enroll 3 patients

Go to next higher dose level or same dose if highest dose

level

Enroll 3 additional ptsat the same dose level

Go to next lower dose levelor declare MTD at next lower

dose level if 6 pts already tested (never re-escalate)

Go to next lower dose levelor declare MTD at next lower

dose level if 6 pts already tested(never re-escalate)

Go to next higher untested dose level or

declare MTD otherwise

DLT =0/3 DLT =1/3 DLT >1/3

DLT >1/6DLT =1/6

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 14

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!

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 18Typical priors represent different types of information

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

Bivariate normal prior for (log(),log()) prior for DLT rates p1,p2,…

DLT rates

p1, p2,...,pMTD,...

(uncertainty!)

HistoricalData

(prior info)

Model based dose-DLT

relationship

Trial Data

0/3,0/3,1/3,...

Clinical

Expertise

Dose recommen-

dations

Decisions

Dose Escalation

Decision

Model Inference Decision/Policy

Responsible: Statistician Responsible: Investigators/Clinician Informing: Clinician (Prior, DLT) Informing: Statistician (risk)

Clinically driven, statistically supported decisions

Summary of statistical component 20

ModelPrior

Expertise

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.

Input Inference Recommendations

DLT rates

p1, p2,...,pMTD,...

(uncertainty!)

Trial Data0/3@1 mg

Model based dose-DLT

relationship

HistoricalData

(prior info)

ClinicalExpertise

Dose recommen-

dations

DecisionsDose Escalation

Decision

Additionalstudy data

(e.g. AE, labs, EKG,PK, BM, Imaging

Protocol development

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)

Study conduct

Combination of clinical and statistical expertise 21Practical and logistical aspects

Protocol development (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 single-agent

• 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) 24

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

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

Study conduct 26Patient enrollment / observation for each dose cohort

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, anti-tumor 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 32

Many thanks to my Novartis Oncology BDM colleagues

• Beat Neuenschwander

• Stuart Bailey

• Jyotirmoy Dey

• Kannan Natarajan

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