25
Prognostic Biomarker Qualification: Case Study: ADPKD and TKV Arlene B. Chapman M.D. Director, Section of Nephrology University of Chicago

Case Study: Polycystic Kidney Disease

Embed Size (px)

Citation preview

Page 1: Case Study: Polycystic Kidney Disease

Prognostic Biomarker Qualification: Case Study:

ADPKD and TKV

Arlene B. Chapman M.D.Director, Section of Nephrology

University of Chicago

Page 2: Case Study: Polycystic Kidney Disease

Disclosures

• Research Support: – Boston Scientific

• Consultation– Otsuka, Kadmon, Sanofi-Genzyme

Page 3: Case Study: Polycystic Kidney Disease

ADPKD• 4th leading cause of ESRD• No race/gender favored• >3,000,000 worldwide• Cysts

― Kidneys―Liver ―Pancreas―Spleen―Brain

• Begin in utero• Develop in tubules• Separate from tubules• Isolated sacs

Grantham JJ. N Engl J Med. 2008;359:1477-1485.

Page 4: Case Study: Polycystic Kidney Disease

ADPKD Progression

0

20

40

60

80

100

0 10 20 30 40 50 60

Kid

ney

func

tion

(%)

Age (years)Torres Mayo <aupCP1047707-9

Concentrating defect, Hypertension, Proteinuria

Pain, Hematuria, Stones, Infections

Page 5: Case Study: Polycystic Kidney Disease

025

5075

100

125

150

175

200

Num

bers

Rea

chin

g O

utco

me

0 - 10 10 - 15 15 - 20 20 - 25 25 - 30 30 - 35 35 - 40 >=40Age of Onset

HypertensionGross HematuriaUrinary Tract InfectionsNephrolithiasis

Cumulative Numbers of Complications

0.0

00

.25

0.5

00

.75

1.0

0F

ract

ion N

ot R

each

ing O

utc

om

e0 5 10 15 20 25 30 35 40 45 50 55

Age of Onset

HypertensionGross HematuriaUrinary Tract InfectionsNephrolithiasis

Kaplan-Meier Survival Estimates

ADPKD patients suffer renal complications prior to loss of kidney function

By age 30, over 50% have at least one complication

NIH CRISP Studies; Rahbari-Oskoui, ASN Renal Week, 2010.

Page 6: Case Study: Polycystic Kidney Disease

Characteristics of ADPKD That Associate with ESRD

• Genotype: > 95% PKD1 individuals demonstrate renal cysts by age 30

• Hypertension: occurs in 60% with intact renal function by age 30

• Proteinuria: is not a common feature of this disease, but has important prognostic implications

• Gross hematuria: > 50% will have had an episode by age 40

ALL CHARACTERISTICS HAVE NOW BEEN SHOWN TO MEDIATE THEIR RISK

THROUGH KIDNEY VOLUME

Page 7: Case Study: Polycystic Kidney Disease
Page 8: Case Study: Polycystic Kidney Disease

Inter-observer variability: 2.1%

Intra-observer variability:2.4%

Day-to-day variability: 2.4%

Page 9: Case Study: Polycystic Kidney Disease

Increased Kidney Volume is Due to Increased Cyst Volume

Measurement variability= Inter-observer 2.1%, Intra-observer 2.4%, Day-to-Day 2.4%Grantham, NEJM CRISP 2006; Chapman Kidney Int 64; 1035–1045, 2003

Total Kidney Volume Total Cyst Volume

Kidney growth is highly variable and each individual has their own growth curve

Page 10: Case Study: Polycystic Kidney Disease

Change in Kidney Volume Precedes Change in Kidney Function

1.0

0.5

0.0

-0.5

-1.0Aver

age

Stan

dard

ized

Uni

t1

Cha

nge

from

Bas

elin

e

0 1 2 3 4 6 8Years of follow-up

GFR

htTKV

p<0.05 for htTKV change from baseline; # p<0.05 for GFR change from baseline; htTKV=Height-adjusted total kidney volume; 1 Percent Change Standardized to a common unit; NIH CRISP Studies; Chapman CJASN 7:479, 2012

Page 11: Case Study: Polycystic Kidney Disease

Baseline predictors of CKD Stage 3 endpoint

Variable Units AUC Sensitivity Specificity Cut-point95%CI of

AUC P*

htTKV cc/m 0.84 0.74 0.7 600 (0.79, 0.90)

Serum Creatinine mg/dL 0.75 0.58 0.81 1.1 (0.67, 0.82) 0.02BUN mg/dL 0.76 0.63 0.79 16 (0.70, 0.83) 0.04Urine Albumin mg/d 0.70 0.66 0.67 30 (0.61, 0.78) 0.002MCP-1 pg/mg 0.75 0.80 0.62 410 (0.68, 0.83) 0.02Baseline age y 0.66 0.60 0.65 35 (0.59, 0.74) < 0.001

95% CI = (0.79, 0.90)Sensitivity = 74%Specificity = 75%Cut Point = 600 (cc/m)

0.00

0.25

0.50

0.75

1.00

Sens

itivity

0.00 0.25 0.50 0.75 1.00

1 - Specificity

AUROC = 0.84

Page 12: Case Study: Polycystic Kidney Disease

QUALIFICATION OF TOTAL KIDNEY VOLUME AS A PROGNOSTIC BIOMARKER FOR USE IN

CLINICAL TRIALS EVALUATING PATIENTS WITH AUTOSOMAL DOMINANT POLYCYSTIC

KIDNEY DISEASE

RD Perrone, JF Marier, MS Mouksassi, F Czerwiec, K Romero, E Dennis, D Miskulin, A Chapman, B Gitomer, and

VE Torres for the PKD Outcomes Consortium

Page 13: Case Study: Polycystic Kidney Disease

Primary Research Objectives Determine the predictive value of TKV, baseline eGFR,

baseline age and other prognostic factors (e.g., sex, PKDmutation, race) in estimating the risk of worsening of eGFRand ESRD to support the regulatory qualification of TKV as aprognostic biomarker for use in clinical trials.

Develop a joint model that can simultaneously assesslongitudinal TKV measurements and the probability of diseaseoutcome. Use the above joint model as a drug developmenttool (DDT) for trial enrichment strategies.

Page 14: Case Study: Polycystic Kidney Disease

Decision Tree for Use of Baseline TKV for Prognostic Clinical Trial Enrichment

Patient Selection for Clinical Trials

Candidate Endpoint:30% Worsening of eGFR

Trial and Inclusion Criteria Early Outcome Trial

W ml < TKV < X mL, age (range)

Clinical Trial Impact:• Fewer patients• Shorter study duration• Reduced clinical trial costs• Reduced exposure to potential drug toxicities• Improved success rate of clinical drug development• Use to select patients for appropriate clinical trials

eGFR

Goal:Prevention of Early Outcomes

Goal:Reduction of Complications

Goal:Reduce Progression to ESRD

Candidate Endpoint:57% Worsening of eGFR

Candidate Endpoint:ESRD

Trial and Inclusion Criteria Disease Progression Trial

X ml < TKV < Y ml, age (range)

Trial and Inclusion Criteria Late Stage Trial

TKV > Y mL, age (range)

Page 15: Case Study: Polycystic Kidney Disease

CRISP Observational

StudyN=241

Data

ADPKD Common

Data Elements

CDISCSDTM

Define Priority

PKD DataADPKD

Supplement to the SDTM User

Guide

Aggregated Common ADPKD

Research DatabaseN = 2,355

Standardized PKD DataColorado Registry

N=1,112

Mayo RegistryN=1,010

EmoryRegistryN=376

PKDOC Data Overview and Summary

Page 16: Case Study: Polycystic Kidney Disease

Trial Enrichment Using TKV: Highest Risk of Progression

Page 17: Case Study: Polycystic Kidney Disease

Predicted Probability at Baseline of Avoiding a 30% Decline in eGFR: Effect of Baseline TKV

Pred

icte

d Pr

obab

ility

at B

asel

ine

of A

void

ing

a 30

% D

eclin

e in

eG

FR

Page 18: Case Study: Polycystic Kidney Disease

Classification of ADPKD patientsPre-specified imaging findings

ClassSubclass

Term Description

1Typical ADPKD

Cyst distribution is bilateral and diffuse with relatively even contribution to TKV

2Atypical ADPKD

A

Unilateral Normal contralateral kidney with ≤2 cysts

Asymmetric Mild involvement of contralateral kidney with 3-9 cysts and <30 % of TKV.

Segmental Involvement only one pole of one or both kidneys

Lop-sided Mild replacement of kidney tissue with ≤5 cysts accounting for ≥50% TKV.

B

Bilateral presentation w/ acquired unilateral atrophy

Atrophy of contralateral kidney.

Bilateral presentation w/ bilateral kidney atrophy

Length < 14.5 cm, atrophy of parenchyma and SCr ≥ 1.5 mg/dL

Irazabal. J Am Soc Nephrol 26: 160–172, 2015

Page 19: Case Study: Polycystic Kidney Disease

0

1

2

3

4

0 1 2 3 4

TKV

Elli

psoi

d (m

L)Th

ousa

nds R2=0.991

TKV Stereology (mL) Thousands

0

2

4

6

8

10

12

0 2 4 6 8 10 12

TKV

Elli

psoi

d (m

L)Th

ousa

nds

Thousands

R2=0.979

TKV Stereology (mL)

Mayo CRISP

Irazabal. J Am Soc Nephrol. 26: 160–172, 2015

Ellipsoid equation (π/6xLxWxD))

TKVe correlates strongly with TKVs

Page 20: Case Study: Polycystic Kidney Disease

100

1000

10000

15 20 25 30 35 40 45 50 55 60 65 70 75 80

HtT

KV

(mL/

m)

Patient Age (Years)

20000

80006000

4000

2000

800600

400

200

Subclass 1A≤1.5%

Subclass 1B1.5 – 3%

Subclass 1C3 – 4.5%

Subclass 1D4.5 – 6%

Subclass 1E> 6%

1E

1C

1A

Classification by Estimated Rate of Growth (from age and starting HtTKV = 150 ml/m)

Irazabal. J Am Soc Nephrol 26: 160–172, 2015

Page 21: Case Study: Polycystic Kidney Disease

1D

Post-Hoc Analysis: HALT PKD Study A Distribution of Patients by Class at Baseline

1A

1B

1C

1E

2

N = 551

Class 1: 94.6%

Class 2: 5.4%

Page 22: Case Study: Polycystic Kidney Disease

Image Classification of HALT PKD Study A Patients

(N = 551)

100

1000

10000

15 20 25 30 35 40 45 50 55 60 65 70 75 80

HtT

KV

(m

L/m

)

Patient Age (Years)

20000

80006000

4000

2000

800600

400

200

Subclass 1E> 6% per year

Subclass 1D4.5 – 6% per year

Subclass 1C3 – 4.5% per year

Subclass 1B1.5 – 3% per year

Subclass 1A≤1.5% per year

Page 23: Case Study: Polycystic Kidney Disease

23

• Class severity associates with greater rates of TKV increase and eGFRdecline

• Changes in TKV and eGFR are negatively correlated

• The treatment effect of low BP increases with class severity

• In the patients with the most severe disease (class D-E), low BP associates with slower eGFR decline after month 4 and overall

• Restriction of enrollment to class 1D-E patients would have detected a stronger low BP effect on TKV growth and EGFR decline, with a much lower number of patients (187 vs 551)

• These results stress the importance of optimal patient selection to reduce the cost and the chance of a type II error

Value of Image Stratification of ADPKDHALT PKD Study A as a Model

Page 24: Case Study: Polycystic Kidney Disease

Interventional trials designed based on disease natural history

Trial PopulationMid-Stage ADPKD

Significant cystic burden for ageTKV ≥ 750 ml Age 18-50

Preserved kidney functionCKD 1-3: eCrCl >60 ml/min

0

25

50

75

100

0 20 40 60Age Of Event Onset

HypertensionGross HematuriaUrinary Tract InfectionsNephrolithiasis

Perc

ent w

ithou

t Eve

nt

Endpoints: Disease Specific Modifiable Outcomes

Cyst Growth by TKV

Kidney Function Decline

Progression related events

Page 25: Case Study: Polycystic Kidney Disease

Thanks for your attention!