Novartis: Challenging to Accelerate
Oncology New Drug Development
Tomoyuki Kakizume
Novartis Pharma K.K.
Oncology Biometrics and Data Management Dept.

Bayesian Clinical Trials in Novartis Oncology
 Phase 1 studyy• To estimate the maximum tolerated dose (MTD)• Global (1998-): > 60 trials, Japan (2008-): 6 trials

 Phase 2 study• Early stopping by futility• >50 trials

 For Novartis oncology P1 studies
studies, Bayesian trials are theglobal standard!

2Outline Whyy do we conduct BayesianyPhase I Trials?
 How do we conduct Bayesian Phase I Trials?
 What benefits can Bayesian Design bring us to?

3

Challenges and Design Requirements in Oncology
Phase I Trials
Phase I Trial Challenges

Design Requirements

High toxicity potential: safety first
Most responses
poccur 80%-120%of MTD

Avoid subtherapeutic doses
while controlling overdosing

PrimaryPiobjective:
bj ti
d t
determinei
MTD

Estimate MTD accurately

Make adequate decisions intimely fashion

Utilize available information
efficiently

4Traditional 3+3 DesignEnroll 3 patients
DLT =0/3

DLT =1/3

DLT >1/3

Go to nexthigher dose level

Enroll 3 additional pts
at the same dose level

Stop dose escalation(de-escalation or stop)

DLT =1/6

DLT >1/6

Go to next
higher dose level

Stop dose escalation(de-escalation or stop)

To determine the next dose, only the number of DLTat the current cohort is used.
5

Bayesian Statistics
Advantages

HistoricalTrials- Different sources of information are combined
probabilistically-Accurate
Accurate prediction can be obtained by putting together
our historical knowledge and ongoing trial dataPublications

ExpertKnowledge

ContextualEvidence+“Prior”

Observed
Data“Likelihood”

6Pr(DLT)⇒“Posterior”

Predictions
Decisions
Derived Quantity⇒=+Pr(DLT)=Updated
Evidence

Pr(DLT)

Pr(DLT)3+3 Design vs Bayesian Design
3+3 Designg

BayesianyDesigng

Usability

Easy - algorithm

More complex - model

Available
information•The number of DLT•Prior information•Ongoing trial data

att currentt cohort
h t

Not flexible
Flexible fixedfi d cohort
h t size
i att 3  adjustable
dj t bl cohort
h t sizei
 fixed doses
 unplanned doses

Fl ibilit
Flexibility
Accuracy of MTD
estimation

LowerHigher•Observed DLT rates•Estimate DLT rates at eachIInference
fforf
true DLT rates

7Bayesian Design cansolvel Ch
Challengeslliin
Oncology Phase I Trials!

doses•Risks of DLT occurrence ateach doses

Challenge in Japan Development<Delayed-Start
Delayed Start Development
Development>
P1

P2

P3

P1

<Simultaneous Development>
P1
P1

8

P2
Accumulateclinical experiencepP3

Outline Whyy do we conduct BayesianyPhase I Trials?
 How do we conduct Bayesian Phase I Trials?
 What benefits can Bayesian Design bring us to?

9Novartis Bayesian Approach to Oncology Phase ITrials
 Assume dose-toxicityy model Update probability of DLT rate at each dose byincorporating prior information (pre-clinical(pre-clinical, human) andobserved data into model AssessAth riskthei k off ththatt theth truet
Mean DLT rate = 25%

DLT rate at each dose
exceeds 33%, given all of ourprior information andobserved data

Risk dose is too toxic= 30%

0.00

010

0.33

0.33
Pr(DLT)

1.00

1Clinically driven
driven, statistically supported decisionsHistoricalData(prior info)
Trial Data
0/3,0/3,1/3,...

DLT rates
p1, p2,...,pMTD,...
(uncertainty!)

Doserecommendations

Model based
dose-DLT
relationship

ClinicalExpertise

Responsible: Statistician
Informing: Clinician (Prior(Prior, DLT)M d lModel11

Decisions
Dose Escalation
Decision

I f
Inference

Responsible: Clinician
Informing: Statistician (risk)

D i i /P li
Decision/Policy

Example of Dose Escalation/De-escalation in
Bayesian Oncology Phase I Trials
Non-DLT

DoseDLT

Dosage range recommended
byy Bayesian
ystatistics5040302010Cohort
112234567

Outline Whyy do we conduct BayesianyPhase I Trials?
 How do we conduct Bayesian Phase I Trials?
 What benefits can Bayesian Design bring us to?

13

Benefits of Bayesian Oncology Phase I Trials
CostQuality

Accurately estimate MTD
Utilize all relevant information
Select next dose clinicallyLittle-affected by DLT occurred by chance
14

Speed

New Challenges
 Combination pphase I studyy• Incorporate each historical SINGLE agent data into prior• Allow flexible dose escalation decisions clinically (dosage/agent)
Agent B

 Global/Asian phase I study

Agent A• Estimate Global/Asian MTD considering the ethnic sensitivity in onetrial

15

Example of Novartis Bayesian Approach toOncology phase II Trials
Endpoint: ORR
Objective: to observe an ORR ≧ 25%
Prior
Information+

Observed
Data=

UpdatedEvidence

Prediction

Low

PredictPdi t ththe ““probabilityb bilit off
success” at the end of thestudy
Pr(Final ORR≧25%|data)

Moderate
0.00

0.25

1.00

ORR
16

Stop
the trialContinuethe trialExpectation to Bayesian Trial Designs
C tCostQuality

Speed

Bayesian study design can maximize the quality of thenew drug development!
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Acknowledgement
Beat Neuenschwander
Stuart Bailey
Masayuki Suzuki
JJunsukek TTaniguchi
ihi
Dai Imamura
Takeshi Tajima
Taro Amagasaki
Asuka Mori
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