Roundtable Update PGt White Paper

PhRMA White Paper on ADME Pharmacogenomics: Survey ResultsLisa A. Shipley, Ph.D.Advisory Committee for Pharmaceutical Science and Clinical PharmacologyMarch 18, 2008

Goals of the White Paper Present a pharmaceutical industry perspective of the recent

and future utility of pharmacogenomics (PGx) related to the ADME properties of drugs for drug development and utilization

Offer perspectives on the current state of practices, strategies, knowledge and key information gaps that need to be addressed in order to fulfill the promise of PGx in targeted medicine

Does not intend to provide best practice for ADME PGx in drug development or utilization, nor to address ethical ramifications of ADME PGx

Second half of paper discusses current understanding of clinically significant polymorphisms of drug metabolism enzymes and transporters

Approach

To establish a cross-industry perspective on the current utility of ADME PGx, PhRMA conducted a survey of recent (2003-2005) major pharmaceutical company practices

The Survey Assembled a series of questions to elicit broad information about

current pharmaceutical company ADME PGx practices Survey respondents were instructed to base all answers on clinical

trials that initiated during the period 2003-2005 Companies were also asked to provide citations for peer-reviewed

original PGx ADME research published by industry scientists (up to 3/company), and examples of how PGx ADME information has been used for internal decision-making and regulatory interactions

PhRMA staff solicited responses from PhRMA member companies and aggregated data, to preserve companies’ anonymity (except for citations of published papers)

Not every company answered every question Some questions have been reordered for ease of presentation

Participating Companies

Abbott Laboratories Amgen Astra-Zeneca Bristol Myers Squibb GlaxoSmithKline Johnson & Johnson Lilly

Merck Millennium Novartis Pfizer Sanofi-Aventis Schering-Plough Wyeth

1. How often has your company collected DNA w/ consent for ADME-related genotyping in:Study Type # of Responses

Always Usually Sometimes NeverFirst in human 2 11 1Multiple rising

dose2 12

Drug-drug interaction

1 11 2

Special population 8 5 1Other clin pharm 11 3Proof of concept 7 7Dose ranging 7 7Pivotal 6 8Other 4 8 1

2. How often has your company performed ADME-related genotyping in:Study Type # of Responses

Always Usually Sometimes NeverFirst in human 1 1 12Multiple rising dose

1 1 12

Drug-drug interaction

1 12 1

Special population 1 11 2Other clin pharm 1 11 2Proof of concept 12 2Dose ranging 1 8 5Pivotal 10 4Other 9 5

3. Has your company used ADME-related genotype(s) in study design?

Inclusion Criterion – 10 yes, 4 no Exclusion Criterion – 10 yes, 4 no

Gene # of Responses

CYP2D6 11

CYP3A5 1

UGT1A1 4

CYP2C19 7

CYP2C9 4

MGMT 0

GSTM1 0

Always Usually Sometimes Never

4. How often has your company specified PG-PK analysis in study protocols?

2 12

7. How often does your company have a written PG-PK plan or strategy for a compound in development?

2 1 9 2

12. How often has your company used phenotyping to ensure that genotype-assigned phenotypes are correct?

6 8

15. How often has the statistical power of PG-PK analysis entered into study design criteria at your company?

9 5

17. How often has your company used any FDA-approved in vitro diagnostic (UGT1A1 kit or CYP2D6/2C19 chip) for PG-PK for clinical trial applications?

6 8

5. Breadth of genotyping. Please check whether your company currently genotypes each gene.

GENE# of ResponsesYes No

CYP1A2 7 4CYP2A6 6 5CYP2B6 6 5CYP2C8 7 5CYP2C9 12 1CYP2C19 13 0CYP2D6 14 0CYP3A4 8 4CYP3A5 11 3Other phase 1 enzyme

6 3

GENE# of ResponsesYes No

UGT1A1 13 1TPMT 3 8Other phase 2 enzyme

8 3

OATP1B1 7 4BCRP 7 4MDR1 9 3Other transporter

7 5

Other PK-related

7 5

6. Has you company only genotyped when preclinical data indicate a role for the gene in a compound’s PK, or do you genotype a broader range of genes?

With preclinical support only: 8

Broader range: 6

8. Has genotyping been done within your company or outsourced?

In-house 5

Outsourced 4

Both 89. How has your company coded samples collected for PG-PK research (ok to check more than one)?

Single Coded: 8Double Coded: 10Anonymized/Anonymous 3

YES NO

10. Has your company kept/banked DNA beyond the initial period of the clinical trial?

13 0

13. Has your company combined samples across studies of a single compound to enhance the statistical power of PG-PK analysis?

11 1

16. Has your company used large-scale (e.g., multigene chip-based) exploratory PG-PK analysis?

6 8

18. Does your company apply the following standards for human DNA sample collection and generation of human genotype data that might be used in regulatory submissions?

GCPGLP

GMP

14102

310

11. When PG-PK research has been included in a trial, has it been a required study activity or optional for each subject in: (ok to check both boxes in a row)

STUDY TYPE# of Responses

YES(Required)

NO(Optional)

Phase 1 studies 12 12

Drug interaction studies

10 8



14. How important has replication of a PG-PK finding been in your company? (please check only one box)

# of Responses

An independent replication has always been necessary

1

An unreplicated result has been used for internal decision-making but not in a regulatory submission

7

An unreplicated result based on a known valid biomarker has been used in a regulatory submission

2

An unreplicated result based on another biomarker has been used in a regulatory submission

0

PG-PK results have not been used 2

YES NO

19. Have scientists from your company published original PG-PK research in peer-reviewed journals?

8 6

20. Has PG-PK information been used in decision-making at your company?

11 3

21. Has your company interacted with FDA or other regulators regarding PG-PK?

10 3

Summary of Survey PGx has already significantly impacted drug development and beginning to

influence drug utilization PGx allows the identification, confirmation or exclusion of clearance pathways PGx analyses have utility in

Explaining PK variability Ensure trial population appropriately balanced Ensure the safety of volunteers and patients Provide mechanistic information

PGx studies may support labeling claim concerning PK dosing; ethnic variability and safety

A significant effort remains in the education of the public, prescribers, ethics committees and investigators

Questions or Comments?

Incorporation of Pharmacogenetic in Healthcare

Eric Lai, Ph.D.,VP, Pharmacogenetics

March 18, 2008

19

This presentation represents my personal views and does not necessarily reflect the policies or endorsement of GlaxoSmithKline.

20

What is Pharmacogenetics (PGx)? The field of Pharmacogenetics deals with the

effect of individual gene variants on the action of a given drug.

“Right Drug for the right patient at the right time” or personalized medicine.

21

Misconceptions about PGx There is no such things as a personalized medicine (The

right medicine to the right patient at the right dosage at the right time). This is a perfect example of marketing talk (Drugs are not like cars or computers).

Clinical trials are done on populations, effects are observed in a group of patients.

PGx increase the probability whether the drug is going to be beneficial for you or may have major adverse effort(s) against you.

The drug is not specifically designed for you but for a group of targeted individuals (Targeted or Informed Medicine). So it is more like clothing sizes (whether you are size 0 or 14).

Not all clinical trials or drugs have PGx components

22

PGx results are used to support post-marketing risk management, product differentiation in

market place & asset progression Support post-marketing risk management and product differentiation by

identifying treatment and management opportunities using patient’s marker status (Phase III - IV)

– Improve safety profile: Safety PGx

Support asset progression (Phase II – III)– Improve efficacy profile (Efficacy PGx)– Improve safety profile

Account for variability (Phase I)– Drug safety & efficacy varies between individuals and ethnic groups.

Explain etiology (Disease understanding)– Genetics influences susceptibility to adverse drug reactions. Understanding

these mechanisms can be used to minimize risk.– Genetics influences efficacy of medicines– understanding mechanisms can

be used to select back-up compounds or identify best treatment population.

23

Is PGX ready for prime time? Is the Science of PGx robust enough for routine

application? Is the Pharmaceutical industry ready to incorporate

PGx in post-market drug safety management and/or to improve the probability of success in drug development?

Are the Physicians ready to order PGx testing? Are the Patients ready for PGx testing? Are the payers (e.g. Government, insurance industry,

etc) ready to pay for PGx testing? Are the regulatory agencies up to the task of

implementing PGx in drug development and drug safety?

24

Safety PGx - abacavir hypersensitivity

25

What is abacavir hypersensitivity?

Abacavir (ABC) commonly used in treatment of HIV-1 infection Approved products include Ziagen, Trizivir and Epzicom / Kivexa Generally well tolerated Key limitation: abacavir hypersensitivity reaction (ABC HSR)

– affects ~5% of clinical trial patients

Features of ABC HSR:– Multi-organ clinical syndrome – typically fever and/or rash ±

constitutional, GI and/or respiratory symptoms– Symptoms usually (>90%) occur within first 6 weeks of therapy– The symptoms worsen with continued therapy and can be life-

threatening but usually resolve upon permanent discontinuation of abacavir

– Re-challenge is contraindicated and can be fatal

26

GSK research on ABC HSR Post-approval commitments in 1999 to

regulatory authorities to conduct research to understand ABC HSR and develop tests to confirm diagnosis

Pooled analysis of studies identified some risk factors for HSR: race, sex, CDC class, prior HIV drug treatment, NNRTI co-introduction

Comprehensive & effective risk management program created (educational materials, labelling, pharmacovigilance, etc.)

27

Cumulative patient-years of exposure to ABC products & cumulative spontaneous reports of ABC HSR-associated mortality

0

200

400

600

800

1000

1200

1999 2000 2001 2002 2003 2004 2005 2006

M ortality Patient/years (x10^3)

…but can we determine an individual’s risk for ABC HSR?

28

GSK Abacavir-HSR project timeline 1994 Initiate clinical development program 1998/9 Marketing Approval for first ABC product – Ziagen 1999 Initiation of the GSK Abacavir-HSR project team 2001 Association of HLA-B*5701 with ABC HSR

Caucasian males (50-94% sensitivity, 98% selectivity)

2003 HLA-B*5701 extended to Caucasian females 2004/5 LabCorp offers HLA-B*5701 screening assay

– Response to requests by US HIV clinicians 2006 First reports of prospective HLA-B*5701 screening

on incidence of HSR 2007 Prospective trial (Predict -1) results presented at IAS

29







30

PGx can be used to detect ADRs: Number of Cases Required to Achieve 80% Statistical Power

Adverse Drug Reaction Genetic Risk Factor Cases Required

Drug Reaction Prevalence Risk Allele Freq.1 Effect2

Gefitinib Diarrhea 0.28 ABCG2 Q141K 0.07 5 47 (>150)

Isoniazid Hepatotoxicity 0.15 CYP2E1*1 & NAT2 Slow Ac

0.133 7 122 (>150)

Irinotecan Neutropenia 0.20 UGT1A1*28 0.32 28 26 (58)

Abacavir Hypersensitivity reaction

0.05 HLA-B*5701 0.04 36 15 (19)

Tranilast Hyperbilirubinemia 0.12 UGT1A1*28 0.30 48 42 (54)

Allopurinol Severe cutaneous adverse reactions

<0.001 HLA-B*5801 0.15 678 19 (19)

Carbamazepine Stevens-Johnson Syndrome

<0.001 HLA-B*1502 0.04 1023 9 (9)

1Allele frequency of the ADR susceptibility variant2Genetic effect is the estimate of the genotype relative risk for those homozygous for the susceptible

genotype compared to the low risk homozygotes3Frequency of the CYP2E1*1 and NAT2 slow acetylator homozygous genotype in Europeans4Number of cases required to achieve 80% power to reject the null hypothesis with 200 clinical matched

(population) controls. Bonferroni adjusted α = 0.05. Assumes LD between genetic risk factor and best SNP marker is r2 = 0.7.

31



PGx in post-market drug safety management and/or to improve the probability of success in drug development? Yes -> if they are forced to.




32

0.0

0.05

0.10

0.15

0.20

1998/1999 2000/2001 2002/2003 2004 to July 2005

Pro

porti

on o

f AB

C-n

aïve

pat

ient

s di

scon

tinui

ng A

BC

with

in 6

wee

ks

n=68 n=131 n=102 n=49

1

Before PGxscreening

After PGxscreening

2

P<0.05

Non-GSK study: Prospective HLA-B*5701 screening reduces early discontinuation of ABC

Non-GSK study – Western AustraliaRauch et al. Clin Infect Dis 2006; 43: 99

Discontinuation due to any reasonDiscontinuation due to ABC HSR

1122

7

2

115

33

Determining Clinical Utility: GSK’s Prospective Trial (PREDICT-1) study objectives

To determine whether prospective screening for HLA-B*5701, prior to abacavir treatment, resulted in:– a significantly lower incidence of clinically

suspected ABC HSR– a significantly lower incidence of immunologically

confirmed ABC HSR as determined by ABC skin patch testing

Mallal S et al. NEJM, 2008

34

PREDICT-1 results published Feb 7, 2008

7.8%

2.7%3.4%

0.0%0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

Prop

ortio

n of

pat

ient

s w

ith A

BC

HSR

(%)

Control ControlScreen Screen

Clinically suspected ABC HSR

P<0.0001Odds Ratio 0.4 (CI: 0.25, 0.62)

P<0.0001Odds Ratio 0.03(CI: 0.00, 0.18)

Clinically suspected ABC HSR plus skin patch test positive

n=847 n=803 n=842 n=802

35

PREDICT-1 conclusions Prospective HLA-B*5701 screening and avoidance of

ABC in patients who were HLA-B*5701 positive– significantly reduced the incidence of clinically suspected ABC HSR– eliminated immunologically confirmed (i.e., patch test-positive) ABC

HSR

HLA-B*5701 positive patients are at increased risk for ABC HSR

HLA-B*5701 negative patients are at reduced risk for ABC HSR

Mallal S et al. NEJM, 2008

36







37

Volume of HLA-B*5701 testing:Experience of one clinical laboratory

LabCorp communication

Presentation of PREDICT-1 and SHAPE data at AIDS conference

PREDICT-1 publication in NEJM

Gro

wth

38







39

Common Sense and Predictably Irrational behavior

Odds of winning in a casino.– Will you pay for a test if the result can tell you

whether you can improve your odd of winning from 5% to 50%? >80% of the people will say yes

Odds of getting an adverse drug reaction.– Will you let your physician order for a test if the

result can tell you whether you have a higher chance of getting an adverse drug reaction (from 5% to 50%)? Only about 50% of the people will say yes

40







41

Efficacy PGx

42

Current Drug Development Process Current drug development and approval processes

center on data collected from research participants

Most drugs are effective in a majority of patients (Spear, B. Trends Mol Med May 2001 7 (5) 201-204) :

Alzheimer 30%Asthma 60%Cardiac Arr. 60%Depression 62%

All drugs have side effects and some drugs produce major adverse reactions in small subset of patients

43

Examples of Efficacy PGxDrug Indication Efficacy measure Gene-allele FDA

guidelinesCetuximab Colorectal cancer Disease-free survival EGFR+ Required

Trastuzumab Breast Cancer Disease-free survival Her2 overexpression

Required

Lapatinib Breast cancer Disease-free survival Her2+ Required

Tamoxifen Breast cancer (high risk of recurrence)

Reduced relapse-free time CYP2D6*4/*4 For information

only

Busulfan Myelogenous leukemia Disease-free Philadelphia chromosome +

For information

only

Carvedilol Heart failure Ejection fraction; survival 1-AR (arg389) none

Pravastatin Dyslipidemia HDL and total cholesterol levels, atherosoclerosis progression

CETP-B1HMGCR (HAP7)

none

Donepezil Alzheimer’s Disease Improvement in ADAS-Cog ApoE4+ none

Rosiglitazone Alzheimer’s Disease Improvement in ADAS-Cog ApoE4- none

44







45

Are current drug development process and regulatory thinking ready for Efficacy PGx?

46

Last thought: what is most important?

47

History of mobile phone development

Importance of the regulatory agency:– The basic concept of cellular phones began in 1947, when researchers

looked at crude mobile (car) phones and realized that by using small cells (range of service area) with frequency reuse they could increase the traffic capacity of mobile phones substantially. However at that time, the technology to do so was nonexistent.

– Anything to do with broadcasting and sending a radio or television message out over the airwaves comes under Federal Communications Commission (FCC) regulation. A cell phone is a type of two-way radio. In 1947, AT&T proposed that the FCC allocate a large number of radio-spectrum frequencies so that widespread mobile telephone service would become feasible and AT&T would have a incentive to research the new technology. We can partially blame the FCC for the gap between the initial concept of cellular service and its availability to the public. The FCC decided to limit the amount of frequencies available in 1947, the limits made only twenty-three phone conversations possible simultaneously in the same service area - not a market incentive for research.

48

1968: Change in regulatory landscape The FCC reconsidered its position in 1968. AT&T and Bell Labs proposed a cellular system to the

FCC of many small, low-powered, broadcast towers, each covering a 'cell' a few miles in radius and collectively covering a larger area.

Dr Martin Cooper, a former general manager for the systems division at Motorola, is considered the inventor of the first modern portable handset. Cooper made the first call on a portable cell phone in April 1973.

49

1983: First commercial cellular phoneDr.Cooper

Motorola is first to ship a commercial portable cellular phone, the DynaTAC, with a suggested retail price of $3,995

The DynaTac phone weighs 28 ounces, is 13 x 1.75 x 3.5 inches in dimension, boasts one hour of talk time and eight hours of standby time and has the nickname of “brick phone”.

50

0

50,000,000

100,000,000

150,000,000

200,000,000

250,000,000

1980 1985 1990 1995 2000 2005 2010

Year

# of

US

user

s$299 with better and more reliable coverage

Cell Phone Subscribers in the U.S., 1985–2005

51

So what did the cell phone industry teach us?

Regulatory decisions are critical A common standard (cellular transmission) Reasonable cost for the general public Reliable and good coverage of services (US

cell phone companies still have a lot of work to improve on this issue).

52

PGx results are used to support post-marketing risk management, product differentiation in

market place & asset progression Support post-marketing risk management and product differentiation by

identifying treatment and management opportunities using patient’s marker status (Phase III - IV)

– Improve safety profile: Safety PGx

Support asset progression (Phase II – III)– Improve efficacy profile (Efficacy PGx)– Improve safety profile

Account for variability (Phase I)– Drug safety & efficacy varies between individuals and ethnic groups.

Explain etiology (Disease understanding)– Genetics influences susceptibility to adverse drug reactions. Understanding

these mechanisms can be used to minimize risk.– Genetics influences efficacy of medicines– understanding mechanisms can

be used to select back-up compounds or identify best treatment population.

53

The Long Road to P450 testing Cytochrome P450 proteins with well established common

polymorphisms that affect drug metabolism have been described since 1950s and molecular basis for the polymorphisms have been known since 1980s.

Potential predictors of optimum dose, drug choice and side effect response

– Eg. CYP2D6 & codeine activation, CYP2C9 & warfarin inactivation Why have they not been taken up into clinical practice?

– Complicated gene families and difficult assays– Lack of a standard and “agreed” panel– Lack of regulatory input and guidance– Limited awareness– Feasibility

Access to test Genetic information required at point of prescribing decision?

54

Agreements among 9 Pharma Companies on ADME genes Core List

- “Must Have Genes”- FDA validated- Significant burden of proof- DMET scientist guided- 33 genes (213 markers)

Extended List- “Need to Have Genes”- Probable involvement- Lacking burden of proof of Core- Mostly addition of Transporters- 143 genes (~2,500 markers)

Investigative List- “Like to Have Genes”- Unknown but possible involvement- Very little literature- Modifiers of metabolism- 333 genes (~9,500 markers)

55

56

Core List

57

PGx: Key Stakeholders

Patients

Regulators

Diagnostics and biotech industry

Healthcare Providers

Bioethics & Policy

Organizations

Payers

Government

Drug safety and efficacy are shared responsibilities

Pharma

Central Testing Laboratories

© Pharsight Corporation All Rights ReservedFeb. 1, 2008

On the Use and Value of Drug-Independent Survival Models to Support Clinical Drug Development in Oncology

Rene Bruno, Laurent ClaretPharsight corporationFDA Clinical Pharmacology Advisory Committee Meeting: Quantitative Clinical Pharmacology: Critical Path Opportunities Washington DC, March 18, 2008

© Pharsight Corporation All Rights ReservedMarch, 2008

OutlineOutline

Drug development in oncology

A drug-disease modeling framework to support drug development in Oncology

● Tumor growth model● Survival model

Support to end-of-Phase II development decisions: A retrospective project with capecitabine (Roche)

On the use the FDA NSCLC survival model● A case study based on erlotinib data

Value of the survival simulations

Conclusions

slide 60 © Pharsight Corporation All Rights ReservedMarch, 2008

Drug development in oncologyDrug development in oncologyLots of new drug candidates with new mechanisms of action

● Major advances in understanding the molecular biology and genetics of the disease have led to the so-called “targeted therapies”

● Highly competitive marketEmpirical selection of dose and dosing schedules

● MTD vs. biologically active dose paradigm in Phase I● Phase II studies not designed to assess dose-response

• Typical randomized Phase IIb dose-ranging studies are not conducted in oncology

● Analysis of clinical trial data poorly informative (e.g. response rate, neutropenia grade…)

Limit the ability to learn from early clinical trialsHigh failure rate in Phase III


A drug-disease modeling framework to predict A drug-disease modeling framework to predict clinical endpoints and support oncology drug clinical endpoints and support oncology drug developmentdevelopment

Models / Endpoints

Dose Exposure

Survival

Tumor size dynamics

Dose-reductions

PFS

ORRDLT Biomarkers

PK / MOA / Resistance covariates, prognostic

factors, gene expression, protein profile


The exposure - tumor size – survival model: A bridge The exposure - tumor size – survival model: A bridge from Phase II to Phase III endpointsfrom Phase II to Phase III endpoints

Dose Exposure SurvivalTumor size dynamics

Drug specificDisease/Patient

specific

Disease specific

Claret L et al. Model-based predictions of expected anti-tumor response and survival in Phase III studies based on phase II data of an investigational agent. Proc ASCO, 24 (18S), 307s (Abs 6025), 2006.

Phase 2 Phase 3

To predict phase 3 endpoint based on phase 2 endpoint and prognostic factors


The tumor-size model incorporates tumor growth and The tumor-size model incorporates tumor growth and drug effectdrug effect

0y 0L D

t

dy tK y t K Exposure t R t y t y

dtR t e

Claret et al. PAGE 15, (Abstract 1004), 2006 [www.page-meeting.org/?abstract=1004]

KD, : drug specific Y0, KL: disease/patient specific

y(t): Larger diameter at time t (mm), y(0): baseline tumor sizeExposure(t): Exposure at time t (dose, AUC…))R(t): Resistance function decreasing with time, ranging from 1 (no

resistance) to 0 (no more drug action): Rate constant of resistance appearance KL: Tumor growth rateKD: Drug constant-cell-kill rate


Goal: To support early drug development decisions● Go/No go● Design of Phase III studies

Simulate expected survival difference in Phase III● Comparing a new drug (X) to a reference drug (R)● Based on Phase II data of X and historical data of R

Retrospective project: ● To simulate:

• Phase III of capecitabine (X) + docetaxel (R) vs. docetaxel in MBC• Phase III of capecitabine (X) vs. 5-Fu (R) in CRC

Claret L et al. Model-based predictions of expected anti-tumor response and survival in Phase III studies based on phase II data of an investigational agent. Proc ASCO, 24 (18S), 307s (Abs 6025), 2006.

Support to end-of-Phase II development Support to end-of-Phase II development decisions: decisions: A retrospective project with capecitabine (Roche)A retrospective project with capecitabine (Roche)


Model parameter estimation● Capecitabine data

• Phase II (2 studies, 170 patients)● Docetaxel data

• Phase III (docetaxel arm, 223 patients)

Simulation● Phase III study of capecitabine + docetaxel vs. docetaxel (443 patients,

1000 replicates)• Assumes additive effect for the combination• Capecitabine scaled from Phase II to Phase III using disease specific

parameters

Focus on efficacy, no model for dose-limiting side-effects● Simulations conditioned on observed dose intensity (dosing history)● Drug effect driven by dose

Simulation of a Phase III study comparing docetaxel Simulation of a Phase III study comparing docetaxel to docetaxel + capecitabine in MBCto docetaxel + capecitabine in MBC


Tumor size reduction relative to baseline

-0.2 0.0 0.2 0.4 0.6

010

020

030

0

-0.2 0.0 0.2 0.4 0.6

010

020

030

0

-0.2 0.0 0.2 0.4 0.6

050

100

200

300

Docetaxel + Capecitabine Arm

Num

ber o

f rep

licat

es

Median25th Quantile 75th Quantile

Observed Predicted 90% PIMedian 0.210 0.270 ( 0.180 - 0.360 )

Simulation of tumor size reduction at week 6 vs. Simulation of tumor size reduction at week 6 vs. observed in the Phase III studyobserved in the Phase III study (1000 replicates)(1000 replicates)


Combination Arm

Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Docetaxel Arm

Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Combination Arm

Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Combination Arm

Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Docetaxel Arm

Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Docetaxel Arm

Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

Simulation of survival vs. observed in the Phase III Simulation of survival vs. observed in the Phase III study study of docetaxel + capecitabine vs. docetaxelof docetaxel + capecitabine vs. docetaxel


Days

Sur

viva

l

0 200 400 600 800 1000

0.0

0.2

0.4

0.6

0.8

1.0

DocetaxelCombination

Observed PredictedCombination 431 412 (330-526)Docetaxel 353 356 (286-443)

Med. Diff 78 57 (-17~148)

Expected survival comparison in a Phase III study of Expected survival comparison in a Phase III study of docetaxel + capecitabine vs. docetaxeldocetaxel + capecitabine vs. docetaxel


The structure of the tumor size model was robust to predict tumor growth and anti-tumor effect of:

● Three cytotoxic drugs in two tumor types Change in tumor size was a good predictor of survival

● Modeling of longitudinal tumor size data is much more informative than response rate determination

● Poor predictor of survival (primary endpoint in Phase III)• Study-level correlations (Buyse Lancet 2000, Shanafelt JCO 2004)• Even more problematic with new targeted therapies (cytostatic rather

than cytotoxic)

The combined tumor size and survival models:● Successfully predicted expected treatment differences● Is a useful approach to support early development decisions:

• Does the expected survival benefit of the new drug warrant further development?

• If yes, which Phase III study need be designed to show non-inferiority, superiority?

Capecitabine project conclusionsCapecitabine project conclusions


Pharsight uses the FDA NSCLC modelPharsight uses the FDA NSCLC modelAvailability of data to develop the survival model is problematic in many companies The availability of generic public-domain models is critical

We used the FDA NSCLC model (Wang et al, DIA, 2007) in one of our projects

● Pharsight Uses FDA Disease Model to Support Oncology Drug Development:http://media.corporate-ir.net/media_files/irol/12/121504/Release112007.pdf

The company was interested in getting expectations of survival for a NCE in combination

● To support decision to start a large Phase III study● They had a Phase Ib combination study in NSCLC (less than 30 patients)

• We used the FDA model and simulated expected survival based on:◦ Observed tumor shrinkage ◦ Patient’s prognostic factors

● The Pharmacometry team (Drs. Wang and Gobburu) provided us the information we needed

The model will be used in several other projects soon

http://media.corporate-ir.net/media_files/irol/12/121504/Release112007.pdf


A case study to illustrate the use of the FDA A case study to illustrate the use of the FDA NSCLC model with erlotinib dataNSCLC model with erlotinib data

Tumor shrinkage data are generally not reported in papers

Karrison, Maitland, Stadler and Ratain recently proposed to use change in tumor size as the primary endpoint in randomized Phase II trials (J Natl Cancer Inst, 99, 1455-1461, 2007)

● In table 1 they report data for change in tumor size from 4 trials● We used data from the pivotal erlotinib trial in 2nd line patients

(Shepherd et al. New Engl J Med, 353, 123-132)• 488 patients in the erlotinib arm• ORR: 8.9%• Survival: 6.7 months


A case study to illustrate the use of the FDA A case study to illustrate the use of the FDA NSCLC model with erlotinib data (cont.)NSCLC model with erlotinib data (cont.)

Data used for the simulation ● Week 8 fractional change data based on Karrison reported log ratio

• The ratio of tumor size at week 8 to baseline size is normally distributed• Log ratio was sampled from normal distribution (Table 1): Mean 0.048, SD: 0.340• Shrinkage = exp(logratio)-1 (distribution given in backup)

● Baseline tumor size distribution• Sampled from lognormal distribution• Mean: 100 mm, SD: 57 mm (distribution in backup)

● EGOG 0, 1 proportion (80/20%) based on observed in Shepherd• Shepherd trial included 66 % ECOG 0, 1 and 34 % ECOG 2, 3 patients (Table 1)• Among ECOG 0, 1 patients, 20% had ECOG 1 and 80% ECOG 2

We simulated 1000 replicates of a virtual treatment arm of 300 patients (second–line, ECOG 0 or 1)

● C and D models (assumed to be second line treatments) were used to simulate 1000 replicates

• 25% of the replicates with C1, C2, D1 and D2• Parameters for each of the replicates were sampled in uncertainty of estimates

● Adjusted with early dropouts


The FDA SSCLC model can be used to simulate expected survival based on tumor shrinkage data Expected median survival: 7.2 months (95% PI: 5.2 to 10.1 months)

● Slightly longer than in Shepherd (was 6.7 months)● But only concerns ECOG 0, 1 patients

Time (month)

Pro

porti

on

0 10 20 30 40

0.0

0.2

0.4

0.6

0.8

1.0

1000 replicates of 300 patientsMedian 95% prediction interval


The survival probability distribution of an investigational treatment can be quantified based on early tumor shrinkage clinical data (typically available in Phase Ib or II)

● Can be a new NCE● Can be a new combination treatment

An arm of the investigational treatment can be simulated conditional on a sample size

● To mimic a clinical trial arm

These simulations can be compared to a survival distribution from a reference treatment

● Expected treatment arm difference can support • Go/no go decision• Phase III clinical trial design

Phase III clinical trials can be simulated to assess probability of success

Value of the survival simulationsValue of the survival simulations


Change in tumor size is a good predictor of survival● Response rate (the primary endpoint in Phase II) is a poor predictor of survival

• Study-level correlations (Buyse Lancet 2000, Shanafelt JCO 2004)● Modeling of longitudinal tumor size data is much more informative than

response rate determination to predict clinical benefit● Supports use of change in tumor size as a primary endpoint in Phase II studies

Drug-independent survival models allow to predict survival expectations or simulate Phase III trials based on early Phase Ib or Phase II data

● Availability of these models is limited● FDA is in a unique position to develop such models without disclosing

proprietary information● Models for other endpoints (e.g. PFS) might be needed

A modeling framework combining longitudinal tumor size models and drug independent survival models can be used

● To predict expected treatment efficacy (ORR, Survival, PFS) ● To simulate clinical trials● To support

• Drug development decisions • Clinical trial design• Drug registration

ConclusionsConclusions


Contributors to the Roche project● P. Girard, Pharsight corporation, now with INSERM, University of Lyon,

France● K. Zuideveld, K. Jorga, J. Fagerberg, F. Sirzen, M. Abt, F. Hoffmann-La

Roche J. O’Shaughnessy, Baylor-Sammons Cancer Center● P. Hoff, MD Anderson Cancer Center● E. Van Cutsem, University Hospital Gasthuisberg● J. Blum, US Oncology Dallas

FDA pharmacometrics team● Y. Wang and J. Gobburu

AcknowledgementsAcknowledgements

Documents

Roundtable Update PGt White Paper