Disease Models

Overview and Case Studies

Joga Gobburu

Pharmacometrics

Office Clinical Pharmacology,

Office of Translational Sciences, CDER, FDA

Pharmacometrics Survey• Between 2000-2006, 72 NDAs needed

Pharmacometrics Reviews/Analyses• For each of the Pharmacometrics Reviews,

the ‘customers’ were asked to rate the impact on approval related and labeling decisions:– Pivotal: Decision would not have been the same

without Pharmacometrics analysis– Supportive: Decision was well supported by the

Pharmacometrics analysis– No Contribution: No need for the

Pharmacometrics analysis

Impact of Pharmacometrics Analyses 2000-2004

Bhattaram et al. AAPS Journal.  2005; 7(3): Article 51. DOI:  10.1208/aapsj070351

Impact Approval Labeling

Pivotal 54% 57%

Supportive 46% 30%

No Contribution 0 14%

Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review

Pivotal: Regulatory decision will not be the same without PM reviewSupportive: Regulatory decision is supported by PM review

Impact →Discipline

Approval Labeling

PM Reviewer 95% 100%

DCP Reviewer 95% 100%

DCP TL 90% 94%

Medical Reviewer 90%@ 90%@

DCP=Division of Clinical Pharmacology@=survey pending in 1 case

Impact of Pharmacometrics Analyses 2005-2006

NDA#1: Approval of monotherapy oxcarbazepine in pediatrics for treating partial

seizures using prior clinical data

FDA/Sponsor pursued approaches to best

utilize knowledge from the previous trials to

assess if monotherapy in pediatrics can

be approved without new controlled trials

• The sponsor was pursuing an accelerated approval, for drug to prevent a life-threatening disease, based on a biomarker even though clinical endpoint analysis failed in two pivotal trials

NDA#2: Establishment of biomarker-outcome relationship allowed more efficient

future trial design

NDA#2: Establishment of biomarker-outcome relationship allowed more efficient

future trial design

0.0 0.5 1.0 1.5 2.0

Ratio of Baseline Anti-dsDNA Levels

01

23

Rel

ativ

e R

isk

of R

enal

Fla

reStudy 09

Estimated RRLL of 95% CLUL of 95% CL

Ratio of biomarker level to baseline

Hazard ratio=10.0 (95% CI 2.5-30.0)

p<0.001Rel

ativ

e ri

sk o

f th

e d

isea

se e

ven

t

0.5

1.6

NDA#3: Insights into trial failure reasons will lead to more efficient future trials

0 5 10 15 20 25 30Dose, mg

-40

-20

0

20

40

60

80

Pla

cebo

-Sub

trac

ted

Cha

nge

In

Sco

re A

at W

eek

12

0 5 10 15 20 25 30Dose, mg

-40

-20

0

20

40

60

80

Pla

cebo

-Sub

trac

ted

Cha

nge

In

Sco

re A

at W

eek

12

Mild Baseline DiseaseNon-Responders

Severe Baseline DiseaseResponders

Females seem to be more sensitive to QT prolongation

Slo

pe

Slo

pe

Slo

pe

Slo

pe

Need/Opportunities for Innovative Quantitative Methods in Drug Development

Optimal design to show ‘disease modifying’ effects?

Good marker(s) of survival benefit in cancer patients?

Maximize the change of success of a 2yr obesity trial?

Given 85% of depression trials fail, how to improve success?

Best dose for a 26wk trial based on 12 wk data?

Providing solutions for these issues callsfor efficient use of prior knowledge

Manage and Leverage Knowledge

Knowledge

Placebo & Disease Models

Information• Biomarker-Endpoint • Time course• Drop-out• Inclusion/Exclusion criteria (Trial)

• Parkinson’s• Obesity, Diabetes• Tumor-Survival• Rheumatologic condition• HIV• Epilepsy• Pain

We are referring to such diverse quantitative approach(es) as ‘Disease Modeling’

Core Development Strategy for Testosterone Suppressants

Disease Model

Reporter Gene Assay

Preclinical

Clinical Trial

Simulation

Dose optimization

in cancer patients

Pivotal trial

|----*2 mo-----|*Actual execution time.- it does account for time spent accumulating resources.

|----*2 mo-----||----*2 mo-----||----*3 mo-----||---------*12 mo--------------|

- Early screening of compounds based on IC50

value.

- High thr’put method to filter thousands of compounds

- Based on prior experience, a few potential entities will be selected for the next phase

IC50

PKPD data

- In vitro IC50 as a guide for preclinical dose selection

- Animal models to measure all possible biomarkers e.g. GnRH, LH, T and Drug conc.

- Invitro and preclinical data for clinical dose and regimen selection

- Clinical development plan

- Pilot study for dose optimization thr’ innovative trial designs

PKPD data

From Pravin Jadhav, VCU/FDA

Obesity

• Obesity trials are large, over 1-2 yrs and fraught with challenges due to high drop-out rate

Dr. Jenny J ZhengDr. Wei QiuDr. Hae Young Ahn

Obesity

Baseline Body Weight

3000 patients

Model Qualification

0

5

10

15

20

25

30

1 2 3 4

Week

Dro

p-o

ut,

%

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

Mean

weig

ht ch

ang

e, kg

0-12 12-24 24-36 36-52

Drop-out patients

Remaining patients

Patients with small weight loss drop-out

Obesity: Time Course of Placebo Effect

0.0

0.4

0.8

1.2

1.6

2.0

0 100 200 300 400

Days

Wei

gh

t L

oss

, kg

Value to Drug Development

• Effective use of prior data for designing future registration trials

• Might lead to alternative dosing considerations– Titration vs. fixed dose– Could lead to increased trial success

• Allows of designing useful shorter duration trials for future compounds for screening and initial dose range selection

Diabetes

• How to reliably select doses for registration trials based on abbreviated dose finding trials

• Need arose from an EOP2A meeting– Work in progress: No patient population and

drop-out models yet.

Drs. Vaidyanathan, Ahn, Yim, Zheng, Wang,

Gobburu, Powell, Sahlroot, Orloff

Pivotal Trial Dose Selection: Anti-Diabetic

• Sponsor conducted 12 wk dose ranging trial in diabetics

• Key Regulatory Question– What is a reasonable dose range and

regimen for the pivotal trial(s)?

• Challenge– Estimate of effect size on HbA1c at 26

wks not available. Effect size on FPG available.

FPG

HbA1c

)1(50

max

CEC

CEKout

inK

inK ' outK '

cHbAKFPGKdt

cdHbAoutin 1''

1

Hb

Alc

FP GD

rug

Conc.

Time (Week)

FPGCEC

CEKK

dt

dFPGoutin

)1(50

max

Cmt 1 Cmt 2

1st order Oral Absorption

FPG-HbA1c relationshipfrom historic studiesemployed to estimateeffects on HbA1c of thenew compound

Jusko et al

Biological relationship between FPG-HbA1c bridged information gap

Week

Ob

serv

ed F

PG

(m

g/d

L)

-10 0 10 20 30 40

100

150

200

250

300

Week

Ob

serv

ed H

bA

1c

(%)

-10 0 10 20 30 40

67

89

10

Week

Ob

serv

ed F

PG

(m

g/d

L)-10 0 10 20 30 40

100

120

140

160

180

200

220

240

260

Week

Ob

serv

ed H

bA

1c

(%)

-10 0 10 20 30 40

67

89

10

11Week

Ob

serv

ed

FP

G (

mg

/dL

)

-10 0 10 20 30 40

10

01

50

20

02

50

30

0

WeekO

bse

rve

d H

bA

1c

(%)

-10 0 10 20 30 40

67

89

10

11

+ =

Drug X (Sponsor) in 72 patients

Drug X (other)in 28 patients

Hybrid datasetin 100 patients

Value to Drug Development

• More informed dose/regimen selection– Could lead to increased trial success

• Quantitative analysis was critical

• Effective use of prior data for predictions

• Supports conduct of useful shorter duration trials for future compounds

Disease Models: Challenges

• Data Management– How to best maintain an efficient database?

• Analysis– How to best conduct meta-analysis?– Identify and fill gaps (time-varying biomarkers

in survival models)?• Inter-disciplinary collaboration

– Biologists, Pharmacologists, Statisticians, Disease Experts, Quantitative Clinical Pharmacologists, Engineers need to come together to develop these models as a team.