2014-10-22 EUGM | ROYCHAUDHURI | Phase I Combination Trials

Satrajit Roychoudhury
East User Group Meeting 2014
Oct 22nd 2014
A Bayesian Industry Approach to
Phase I Combination Trials
in Oncology

 Beat Neuenschwander
 Alessandro Matano
 Zhongwen Tang
 Simon Wandel
 Stuart Bailey
Statistical Methods in Drug
Combination Studies, Boca
Raton, FL: Chapman &
Hall/CRC Press (2015)
Book Chapter and Authors
Acknowledgements
2 | East User Group Meeting 2014 | Satrajit Roychoudhury | Oct 22, 2014 | Phase I Combination Trial

Table of Content
1. Introduction
2. Phase I Design Framework
3. Methodology
4. Applications
5. Implementation Issues
6. Conclusion

1. Introduction
 In Oncology, challenges in Phase I trials are many: while
keeping patient safety within acceptable limits, the trials
should be small, adaptive, and enable a quick declaration
of the maximum tolerable dose (MTD) and/or
recommended phase II dose (RP2D).
 The objective of this chapter is to provide a
comprehensive overview, which includes;
• a rationale based on general clinical and statistical considerations,
• a summary of the methodological components, and
• applications

1. Introduction
Challenges and Design Requirements
Phase I Trial Challenges Design Requirements
Untested drug or combination in
treatment-resistant patients
Escalating dose cohorts with small
number of patients (e.g. 3-6)
Primary objective: determine
Maximum Tolerated Dose (MTD)
Accurately estimate MTD
High toxicity potential: safety first Robustly avoid toxic doses
(“overdosing”)
Most responses occur 80%-120% of
MTD
Avoid sub therapeutic doses while
controlling overdosing
Find best dose for dose expansion
(which generally becomes the
recommended phase II dose)
Enroll more patients at acceptable, active
doses (flexible cohort sizes)
Complete trial in timely fashion Use available information efficiently

2. Phase I design framework
Traditional 3+3 design
Trial Data
0/3,0/3,1/3,...
Dose escalation
decision from
predefined
algorithm
 Very simple BUT
• Poor targeting of true MTD
• Highly variable estimates
 High attrition rates in phase II and III
• How many failures are due to the wrong dose coming
from phase I?

Why Bayesian modeling?
 Bayesian approach offers a way to quantify our
knowledge and assess risk.
• Given what I have seen, what can I say about the true risk of dose
limiting toxicity (DLT)?
 Models allow us to share information between doses –
efficient use of data.
 Flexibility to include additional patients, unplanned doses,
change schedules.
 Higher chance to find the correct MTD (Rogatko et al.
(2007)).

Clinically driven, statistically supported decisions
DLT rates
p1, p2,...,pMTD,...
(uncertainty!)
Historical
Data
(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)

3. Methodology
Single Agent Model (Neuenschwander et. al. 2008)
 Data: (#DLT/#Patients): 𝑟𝑑~ Binomial(π 𝑑, 𝑛 𝑑)
 Parameter Model: logit 𝜋 𝑑 = log(α) + β log(d/d*)
 Prior: (log(α), log(β)) ~ N2(m1, m2, s1, s2, corr)
Model parameter α and β can be interpreted as;
 α is the odds, of a DLT at d*, an arbitrary scaling dose.
 β > 0 is the increase in the log-odds of a DLT by a unit
increase in log-dose.
 Can be extended by adding additional covariates/relevant
predictors representing different patient-strata.

3. Methodology
Developing drug combinations: Paradigm change from single agent
 There is no longer one MTD but a many
- e.g. given drug A = X mg, MTD of drug B is Y mg.
- Critical to determine the MTD boundary and the set of acceptable
doses.
 Still in “learning phase”
- Flexibility (e.g., variable cohort sizes, split cohorts).
- Bayesian model summarizes knowledge on all dose pairs, sets
upper bound on dosing.
- Actual decisions use additional information (e.g. efficacy, PK,
biomarkers, later cycle AE) to select “best” dose pair(s) for next
cohort.

3. Methodology
Interaction: Antagonism, independence or synergy
 There are a number of factors that may lead to increased or
decreased risk of toxicity;
• Drug-Drug Interaction seen in the PK profiles, e.g.
- AUC or Cmax for one or both compounds is significantly
increased/decreased compared to being given alone.
- May be competitively using/inhibiting CYP enzyme causing lower clearance.
• Overlapping toxicities, e.g.
- If drug 1 and drug 2 have similar toxicity profiles, giving them together may
cause a significant increase in the observed rate.
- Equally, the combination may cause the same toxicity in the same subset of
patients but at a higher grade, whilst other patients are unaffected.
- Two toxicities that, when they occur together increase the risk of DLT in a
patient.
• Pathway interactions, feedback loops that either trigger additional
toxicities or provide protective effects.

3. Methodology
Dual and Triple Combination Model
 Proposed model has two parts: (multiplicative odds model)
• Marginal effects: 2 parameters per agent representing single agent
toxicities.
• Interaction: 1 parameter for Dual interaction (Dual combo model).
: 4 parameters for Dual and triple(extra) interaction
(Triple combo model).
 Extension of Thall et. al. Biometrics (2003).
 Properties for proposed combination models
• Parsimonious since the number of tested dose combinations in
phase I trials is usually fairly small.
• Have easily interpretable parameters similar to Base model.
• Allow for interaction.
• Have the ability to incorporate single-agent information.

3. Methodology
Metrics for Dosing Recommendations
 Posterior Distribution: The result of the Bayesian
analysis is the posterior distribution of
which, in turn , leads to the posterior of for each dose
group. Our approach relies formally on this metric (using
principle of escalation with overdose control (EWOC)) for
suggesting feasible dose for next cohort.
 Predictive Distribution: The predictive distribution of the
number of DLT “r*” in the next cohort with size “n”. This
metric is used informally in discussions with clinical teams.
 A Formal Decision Analysis: We do not use formal
decision analysis to identify the dose range for further
dosing or the MTD/RP2D.

3. Methodology
Choice of Prior: Using Contextual Information or Co-data
 Key question: Can we use available information as prior?
 Naive views
• The information is of no value because we simply don‘t know what‘s
going to happen in our trial
• Or, transform these data into a prior that is worth N patients
• Eg. N=200, with observed rate = 0.10. Use a Beta(20,180) prior.
 Between-trial heterogeneity  discounting historical data
• After discounting, prior may be Beta(2,18)
• Don‘t use 10% as a rule of thumb for discounting!
 Methodology: meta-analytic-predictive (MAP)
• Random-effects meta-analysis of historical data/parameters 1,... H
• with a prediction for the parameter * in the new trial
• basic assumption: exchangeable parameters

3. Methodology
Meta-analytic-predictive (MAP) Prior
Similarity Scenario
 H Historical Trials with Effects 1,..., H
 Data from Historical Trials Yh h=1,...,H
 Similarity of Parameters 1, ..., H, * ~ N(µ,2)
 Meta-Analytic-Predictive (MAP) Prior * | Y1,..., YH
 Challenge: how large is ? (cannot be inferred if for small H)

3. Methodology
MAP Prior for Oncology Phase I
 Different types of a-priori information in Phase I
• Animal data (2 species, e.g. dog and rat)
Requires discounting of information due to between-species
extrapolation.
• Historical data for same compound in another indication
(with similar toxicity profile), one or more trials.
Requires moderate to substantial discounting of information
due to between-trial/indication extrapolation.
Typically these priors have relatively little weight, 3 to 9 pts.
• Compared to phase I sample sizes of 15 to 30 patients,
this is still relevant information.

3. Methodology
MAP Priors for Phase I Combination Studies
 Prior for single-agent parameters
• Bivariate normal prior: from co-data (historical/external data), with
appropriate discounting due to between-trial variability using MAP.
 Prior for interaction parameter (odds-multipliers) ’s
• Normal (no restriction) or log-normal (synergistic toxicity)
• Pre-clinical/Clinical information can be incorporated in the prior of
interaction parameter. But often no information is available.
Therefore weakly-informative priors are used.

3. Methodology
Robust Mixture Prior
 The use of historical data requires an extrapolation from
the past to the future. Predictions are always difficult.
 What if there is a possibility that the chosen prior will be in
conflict with the actually observed data?
 Possible robustification: MAP Mixture Priors
• Adding a weakly informative component to MAP may
address the issue.
• Mixture weights represent the degree of similarity
between codata and new trial.
• Mixture priors are more robust against prior-data conflict,
and should be used more often (rarely seen in practice).

4. Applications
Practical consideration for Implementation
 What is our starting combination?
• Generally determined ad-hoc... 50% of each MTD?
• What about interaction? Uncertainty?
 No evidence of synergistic
or antagonistic interaction
between the two compounds
• But don’t want to rule it out
• Normal distribution for interaction
parameter:
- Antagonism
- Independence
- Synergy

4. Applications
Dual-Combination Trial: Background
 Combination of two new compounds
• Compound 1: d1 = 3, 4.5, 6, 8; d1* = 3 (all mg)
• Compound 2: d2 = 33.3, 50, 100, 200, 400, 800, 1120; d2* = 960 (all mg)
• Dose-limiting data (DLT): days 1 - 28
• Toxicity intervals for 𝜋 𝑑: [0-0.16), [0.16-0.35), [0.35-1.00]
• EWOC: P(𝜋 𝑑 >= 0.35) < 0.25
 Actual dosing decisions
• must respect EWOC criterion.
• should also acknowledge other relevant information (PK, efficacy, ...).
 Additional design constraints
• Cohort size 3 to 6
• Only one compound's dose can be increased; max. increment of 100%.

4. Applications
Rules to determine MTD
Dose escalations continue until declaration of the
MTD.
A dose is potential candidate for MTD when;
1. At least 6 patients have been treated at this dose
2. This dose satisfies one of the following conditions:
- The probability of targeted toxicity exceeds 50%:
- or, a minimum of number of 15 patients have already been treated
in the trial.

4. Applications
Dual-Combination Trial: Prior derivation
 Relevant (single-agent) data was identified
 MAP approach to derive priors based on this data:
discussion with clinical team about similarity
 No a-priori evidence for interaction between the two
compounds, but considerable uncertainty

4. Applications
Dual-Combination Trial: Full history (1/2)

4. Applications
Dual-Combination Trial: Full history (2/2)
Cohort 1
3 mg / 400mg
0 DLT / 3 Patients
Cohort 2a
3 mg / 800mg
1 DLT / 3 Patients
Cohort 2b
6 mg / 400mg
1 DLT / 3 Patients
Cohort 3a
3 mg / 800mg
1 DLT / 3 Patients
...Cohort 3b
6 mg / 400mg
0 DLT / 3 Patients

5. Applications
MTD Declaration
 3-800: grade 2 adverse events, moderate increase in
magnitude and duration of QT.
 4.5-600: experienced grade 1 and/or grade 2 adverse
events, No QT prolongation. BUT none of the patients had
a relevant tumor lesion decrease.
 6-400: experienced grade 2 adverse events, but easily
managed with appropriate dose reductions, No QT
prolongation. Moreover, encouraging efficacy signals were
observed.
Based on clinical and statistical findings : 6-400 mg was
declared as the MTD and RP2D.
| East User Group Meeting 2014 | Satrajit Roychoudhury | Oct 22, 2014 | Phase I Combination Trial25

4. Applications
Triple-Combination Trial: Background
 Triple combination
• One «backbone», two experimental
• Agent 1: 100, 200, 300, 400 mg
• Agent 2: 10, 20, 30, 40 mg
• Agent 3: 250 mg (fixed)
 Stage-wise approach considered reasonable
• Find MTD for agent 2 + 3
• Escalation of agents 1 and 2 (agent 3 fixed)
 Prior derivation similar to dual combination case.

4. Applications
Triple-Combination Trial: Model assessment (1/3)
 Stage I: Data scenarios for Dual Combination

4. Applications
 Stage II: Data scenarios for Triple Combination

4. Applications
 Operating characteristics: Assumed true toxicity rates
 Metrics

5. Implementation issues
Practical guide
Some of the points that need to be considered when
implementing the proposed Bayesian adaptive design in
clinical practice.
 Discussion with clinical colleagues
 Expertise in Bayesian Statistics
 Computations
 Study Protocols
 Review Boards

Discussion with clinical colleagues
 Discussing important features with clinical colleagues: key
responsibilities of a statistician when designing and
implementing clinical trials.
 Recommended to explain the main features of the
approach using visual illustrations, non-statistical
language, and common sense.
 A good preparation includes a mimicked dose-escalation
meeting using data scenarios to highlight various possible
outcomes.

Expertise in Bayesian Statistics and Computation
 Expertise in Bayesian Statistics: Implementing the
proposed approach requires:
• Technical skills related to the concrete implementation
• the ability to explain in simple language
• Training/workshop/case studies for clinician and statistician
• project-related regular interactions between statisticians and
clinicians
 Computation: Require a tool that allows to extract the
relevant metrics for inference and decision making:
• WinBUGS/R
• JAGS
• Stan

Study Protocols
 Requires more attention when Bayesian methods are used.
 A clear description of the model and prior needs to be
provided in protocol.
 If an informative prior based on external information is
used, the derivation of the prior should be clear in protocol.
 The actual process for dose-escalations needs to be
described as well.
 Further technical details including data scenarios and
operating characteristic may be covered in a statistical
appendix.

5. Implementation Issues
Review Boards
 Experience shows that discussions with HA and IRB/IEC
around these issues lead to a better understanding and
appreciation of the rationale and intent of the proposed
approach.
 “Typical” questions include;
• A 25% risk of overdose is too high.
• The design allows too many patients to be exposed at once to a new
dose level.
• The design must be set up in a way that observation of 2 DLT at a
dose level means that this dose cannot be used again.
• The design makes a recommendation, but the clinician decides the
dose. This implies that a decision may overrule the original
recommendation, and patients may be dosed at unsafe dose levels.

6. Conclusion
 Detailed explanation of phase I (combination) approach
• Concepts
• Mathematical background
• Examples
• Practical implementation
 Covers about 10 years of practical expertise
• Methodological work
• Operational experience (> 100 trials)
• Technical implementation
 Standard at Novartis Oncology (others just taking up)

References (1/2)
[1] Neuenschwander, Matano, Tang, Roychoudhury,
Wandeland Bailey. A Bayesian Industry Approach to
Phase I Combination Trials in Oncology. Statistical
Methods in Drug Combination Studies, Boca Raton, FL:
Chapman & Hall/CRC Press 2015.
[2] Neuenschwander, Branson, Gsponer. Critical aspects of
the Bayesian approach to phase I cancer trials. StatMed
2008.
[3] Rogatko, Schoeneck, Jonas et al. Translation of
innovative designs into phase I trials. JCO 2007.

References (2/2)
[4] Babb, Rogatko, Zacks. Cancer phase I clinical trials:
efficient dose escalation with overdose control. Statistics
in Medicine 1998.
[5] Thall, Lee. Practical model-based dose-finding in
phase I clinical trials: methods based on toxicity. Int J
Gynecol Cancer 2003.
[6] Thall, Millikan, Mueller, Lee. Dose-finding with two
agents in phase I oncology trials. Biometrics 2003.
[7] O’Quigley, Pepe, Fisher. Continual reassessment
method: a practical design for phase I clinical trials in
cancer. Biometrics 1990.

2014-10-22 EUGM | ROYCHAUDHURI | Phase I Combination Trials

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2014-10-22 EUGM | ROYCHAUDHURI | Phase I Combination Trials