PARKINSON’S PROGRESSION MARKERS INITIATIVEPPMI Data BLITZ. Longitudinal . alpha-synuclein measurements in CSF. Brit Mollenhauer and Douglas Galasko, Biologics Working Group. 0 12

PARKINSON’S PROGRESSION MARKERS INITIATIVE

PPMI 2018 Annual Investigators Meeting PPMI Data Blitz

2

• Longitudinal Change of Clinical and Biological Measures in Early PD Presented by Andrew Siderowf (on behalf of Tanya Simuni)

• Cognitive-Psychiatric Disorders: Progression and Predictors Presented by Dan Weintraub

• Longitudinal alpha-synuclein measurements in CSF Presented by Brit Mollenhauer

• Two and Four Year Longitudinal Assessment of DAT Imaging Biomarkers Presented by John Seibyl

• Innovative Statistical Design and Analysis in PD Presented by Chris Coffey

• The Future of Clinical Trials Presented by Karl Kieburtz

• Baseline Predictors of Disease Progression Presented by Andrew Siderowf

OVERVIEW

How can PPMI study inform the design of Parkinson’s disease clinical trials

PPMI 5 year longitudinal data THANK YOU

Tanya Simuni, MD on behalf of PPMI Consortium Parkinson’s Disease and Movement Disorders Center

Northwestern UniversityParkinson Foundation Center of Excellence

6

CHANGE IN MDS-UPDRS TOTAL SCORE FROM BL TO YEAR 1 IN PD SUBJECTS

Subjects that completed BL and Year 1 Visits p-value

Variable All PD Untreated Treated with Treated with Untreated vs. Untreated vs. Levodopa/DA

Subjects at Year 1 Levodopa/DA at Year 1

Other PD Med at Year 1

Levodopa/DA Other vs. Other

(N = 393) (N = 162) (N = 165) (N = 66)

Change in MDS-UPDRS Total Score

<.0001 0.0406 0.0047

N Completed 334 162 106 66

Mean (SD) 7.45 (11.6) 10.73 (10.7) 2.41 (11.4) 7.48 (11.2)

(Min, Max) (-31.0, 60.0) (-17.0, 60.0) (-31.0, 32.0) (-19.0, 41.0)

*Time to evaluation was not collected at the 6 month visit*

Variable Time to OFF Score at Year 16-12 Hours >12 Hours p-value

Change in MDS-UPDRS Total Score 0.4718N Completed 25 81Mean (SD) 0.96 (13.6) 2.85 (10.7)(Min, Max) (-18.0, 31.0) (-31.0, 32.0)

*Treatment status for this analysis is defined as treatment with either levodopa or dopamine agonists*

Year 1 Change in MDS-UPDRS Total Scores by Time to OFF Evaluation in

Treated PD Subjects

MDS-UPDRS OFF vs. ON in Treated PD Subjects

05

101520253035404550

Year 1 Year 2

Tota

l MD

S Sc

ores

Axis Title

Total MDS Scores OFF vs. ON Totals in Treated PD Subjects

Levodopa/DA OFF Levodopa Only OFF

Levodopa/DA ON Levodopa ON

N ParticipantsTreated with Levodopa/DA

Treated with Levodopa Only

OFF ON Off ON

Year 1 106 154 41 73

Year 2 163 235 81 105

P ValuesTreated with Levodopa/DA

Treated with Levodopa only

Year 1 Total 0.076 0.1237Year 1 Part III 0.0055 0.0063Year 2 Total 0.0001 0.005Year 2 Part III <.0001 0.0001

05

101520253035404550

Year 1 Year 2

Part

III M

DS

Scor

es

Axis Title

Part III MDS Scores OFF vs. ON in Treated PD Subjects

Levodopa/DA OFF Levodopa Only OFF

Levodopa/DA ON Levodopa ON

10

SUMMARY OF THE RESULTSSO WHAT ???

»Unique dataset that provides data on the natural Hx of PD progression using clinical scales, imaging and biologics

»Essential data for design of future disease modification trials

»Data completeness is essential for the utility of the dataset

»Thank you !!!!–Coordinators /Investigators –Participants

11

QUESTIONS?

Cognitive-Psychiatric Disorders:

Progression and Predictors

Daniel Weintraub, MD

PD Cognition Over Time

Data courtesy Chelsea Caspell-Garcia.

VariablePD Subjects

Baseline Year 1 Year 2 Year 3 Year 4 Year 5(N = 423) (N = 395) (N = 378) (N = 366) (N = 346) (N = 289)

MOCAN 420 392 374 363 341 283Mean (SD) 27.13 (2.3) 26.30 (2.8) 26.28 (3.2) 26.40 (3.0) 26.43 (3.6) 26.58 (3.6)(Min, Max) (17.0, 30.0) (15.0, 30.0) (9.0, 30.0) (13.0, 30.0) (11.0, 30.0) (2.0, 30.0)

MOCA <26N 420 392 374 363 341 283No 327 (77.9%) 257 (65.6%) 253 (67.6%) 246 (67.8%) 238 (69.8%) 205 (72.4%)Yes 93 (22.1%) 135 (34.4%) 121 (32.4%) 117 (32.2%) 103 (30.2%) 78 (27.6%)

MOCA <21N 420 392 374 363 341 283No 416 (99.0%) 379 (96.7%) 354 (94.7%) 349 (96.1%) 320 (93.8%) 266 (94.0%)Yes 4 (1.0%) 13 (3.3%) 20 (5.3%) 14 (3.9%) 21 (6.2%) 17 (6.0%)

2 scores >1.5 SD below meanN 419 393 374 363 338 277Yes 46 (11.0%) 56 (14.2%) 49 (13.1%) 55 (15.2%) 49 (14.5%) 37 (13.4%)

Site Investigator DiagnosisN 106 271 370 364 340 278Normal 97 (91.5%) 231 (85.2%) 309 (83.5%) 284 (78.0%) 265 (77.9%) 222 (79.9%)Mild Cognitive Impairment 9 (8.5%) 38 (14.0%) 58 (15.7%) 74 (20.3%) 63 (18.5%) 46 (16.5%)Dementia 0 (0.0%) 2 (0.7%) 3 (0.8%) 6 (1.6%) 12 (3.5%) 10 (3.6%)

Comments• Highly educated, motivated and relatively young cohort

• Raises possibility of practice effects and temporary positive impact of introduction of PD meds on cognition

• Good cohort for studying resiliency to cognitive decline• Hopefully dropouts aren’t differentially cognitively impaired

• For dementia• Apply more stringent cut-off for MoCA (<21)• Subset of patients developing significant cognitive

impairment relatively quickly• For MCI

• Apply site investigator diagnosis• Consider using HC norms instead of published norms• Years 5-10 data will be more informative

• Mean age by year 10 will be 72, likely an inflection point

Supporting Evidence

Wyman-Chick. Movement Disorders 2018;10.1002/mds.27335. Pigott et al. Neurology 2015;85:1276-1282.

PD Psychiatric Symptoms Over TimeVariable Baseline Year 1 Year 2 Year 3 Year 4 Year 5

PD Subjects (N = 423) (N = 398) (N = 378) (N = 366) (N = 348) (N = 293)GDS-15

N 423 395 376 366 343 286Not Depressed (<5) 364 (86.1%) 330 (83.5%) 309 (82.2%) 304 (83.1%) 283 (82.5%) 228 (79.7%)Depressed (≥5) 59 (13.9%) 65 (16.5%) 67 (17.8%) 62 (16.9%) 60 (17.5%) 58 (20.3%)

STAI-StateN 422 395 377 364 343 286Score <45 358 (84.8%) 343 (86.8%) 332 (88.1%) 321 (88.2%) 302 (88.0%) 252 (88.1%)Anxious (≥45) 64 (15.2%) 52 (13.2%) 45 (11.9%) 43 (11.8%) 41 (12.0%) 34 (11.9%)

QUIPN 422 395 377 366 343 285No Disorders 335 (79.4%) 342 (86.6%) 301 (79.8%) 281 (76.8%) 257 (74.9%) 210 (73.7%)ICD behaviors (≥1) 87 (20.6%) 53 (13.4%) 76 (20.2%) 85 (23.2%) 86 (25.1%) 75 (26.3%)

MDS-UPDRS Part I ApathyN 423 395 377 366 343 2880-1 412 (97.4%) 366 (92.7%) 342 (90.7%) 338 (92.3%) 309 (90.1%) 251 (87.2%)>1 (Apathy) 11 (2.6%) 29 (7.3%) 35 (9.3%) 28 (7.7%) 34 (9.9%) 37 (12.8%)

MDS-UPDRS Part I PsychosisN 423 395 377 366 343 2880-1 423 (100.0%) 395 (100.0%) 373 (98.9%) 361 (98.6%) 336 (98.0%) 281 (97.6%)>1 (Psychosis) 0 (0.0%) 0 (0.0%) 4 (1.1%) 5 (1.4%) 7 (2.0%) 7 (2.4%)

Healthy Controls (N = 196) (N = 185) (N = 174) (N = 167) (N = 162) (N = 140)GDS-15

Depressed (≥5) 13 (6.6%) 10 (5.4%) 8 (4.6%) 7 (4.2%) 5 (3.1%) 9 (6.6%)STAI-State

Anxious (≥45) 9 (4.6%) 11 (5.9%) 6 (3.4%) 5 (3.0%) 7 (4.3%) 6 (4.4%)QUIP

ICD behaviors (≥1) 37 (18.9%) 37 (20.0%) 29 (16.7%) 27 (16.2%) 24 (14.8%) 21 (15.3%)MDS-UPDRS Part I Apathy

>1 1 (0.5%) 1 (0.5%) 1 (0.6%) 2 (1.2%) 1 (0.6%) 1 (0.7%)MDS-UPDRS Part I Psychosis

>1 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)

Comments• Disorders

• Depression and Apathy: increasing over time• Anxiety: puzzling, but little non-motor fluctuations in

early PD• ICDs and Psychosis: dopamine agonists less

commonly used as initial therapy, long lag time after starting medications (ICDs), cognitively healthy group (psychosis)

• Comparing change in rates over time in PD relative to healthy controls of value• Depression goes from 2x-3x as common in PD as

HCs

Biomarker Definition of cognitive impairmentMoCA<26 or

Change in MoCA scoreCognitive test deficits

(≥2 tests >1.5 SD below mean)

Site investigator diagnosis(PD-MCI or PDD)

DAT imaging - - ↓ ipsilateral caudate*

↓ contralateral caudate§

↓ contralateral putamen§

CSF - - ↓ CSF Aβ 1-42*

Structural MRI (volume)

↓ entorhinal*

↓ superior temporal*§

↓ caudal middle frontal§

↓ lateral orbitofronal§

↓ superior parietal§

- ↓ lateral occipital*

↓ lateral orbitofrontal*

↓ fusiform*§

↓ superior temporal§

Structural MRI(thickness)

↓ precentral§ - ↑ caudal anterior cingulate§

↓ fusiform§

DTI (FA) - - -DTI (MD) - - ↓ inferior cerebellar

peduncle*

Genetics - - COMT val158met (val/val)BDNF val66met (val/val)

Caspell-Garcia et al. PLoS ONE 2017;12(5): e0175674.

# Significant in univariate analysis, survived FDR correction, and significant in multivariable analysis* Baseline biomarker value§ Longitudinal biomarker value

Predicting Future Psychosis in Early PD

19

Reports of Psychotic Symptoms, n

Characteristic 0 (n=285) 1 (n=54) ≥2 (n=84) P value

Scales for Outcomes in Parkinson’s Disease—Autonomic, mean (SD)

8.5 (5.5) 10.2 (6.3) 12.2 (6.5) <0.0001

REM Sleep Behavior Disorder Screening Questionnaire Median Score (IQR) 3 (2-5)* 3.5 (2-6) 5 (3-7)** 0.0003Score >5, n (%) 91 (32.2)* 21 (38.9) 46 (55.4)** 0.001

Epworth Sleepiness Scale Median Score (IQR) 5 (3-8) 5 (3-7) 6 (3-10) 0.03Score ≥0, n (%) 34 (11.9) 6 (11.1) 26 (31.0) <0.001

*n=283.**n=83.

Adjusted for age and sex in multivariate regression, the following baseline symptoms were associated with increased risk of reporting psychotic symptoms on ≥2 occasions:

Greater autonomic symptoms (OR 1.07, P=0.002)

Presence of REM sleep behavior disorder (OR 1.9, P=0.021)

Excessive daytime sleepiness (OR 2.5, P=0.003)

Barrett et al. Neurology. 2018;10.1212/WNL.0000000000005421.

IQR, interquartile range; OR, odds ratio; REM, rapid eye movement; SD, standard deviation

P=0.02

P=0.51

P=0.04

P=0.31

P=0.67

P=0.17

Cholinergic nucleus 4 Cholinergic nuclei 1, 2, and 3

SSRI Use and Cognitive Decline

20Kotagal et al. Annals Neurology. 2018;10.1002/ana.25236.

Hypothesis: Serotonin may modulate Aβ metabolism through up-regulation of alpha-secretase

21

QUESTIONS?

PARKINSON’S PROGRESSION MARKERS INITIATIVE

PPMI Data BLITZLongitudinal alpha-synuclein measurements in CSF

Brit Mollenhauer and Douglas Galasko, Biologics Working Group

0 12 24 36 48 60

Month

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Mea

n St

riatu

m

Report generated on data submitted as of: 04Apr2016

Mean for SWEDD SubjectsMean for Healthy ControlsMean for PD Subjects

Mean Striatum over time

NOTE: Points are only plotted if 5 or more subjects have data at that visit.

23

TRACKING PROGRESS IN TRACKING PARKINSON‘S

0 12 24 36 48 60

Month

0

5

10

15

20

25

30

35

40

45

50

55

60

MD

S-U

PDR

S To

tal S

core


Mean for SWEDD SubjectsMean for Healthy ControlsMean for PD Subjects ONMean for PD Subjects OFF

MDS-UPDRS Total Score over time


Parkinson‘s disease is a progressing disorder bymotor and non-motor symptoms and dopamine transporter imaging

0 6 12 24 36 48 60

Month

1600

1700

1800

1900

2000

2100

2200

2300

Alph

a-Sy

nucle

in



Alpha-Synuclein over time


0 6 12 24 36 48 60

Month

30

35

40

45

50

55

60

Tau



Tau over time


0 6 12 24 36 48 60

Month

300

325

350

375

400

425

450

A-b

eta



A-beta over time


But there is no PD specific progression in biochemical markers in the cerebrospinal fluid in 12 months

24

DISCREPANCY OF CSF ALPHA-SYNUCLEIN MEASUREMENTSBETWEEN 2013 AND 2016 RE-ANALYSIS OF SAMPLES

Possible reasons for this discrepancy:-preanalytical sample handling (e.g. gradient effect)-analytical variability (non-automated performance of >50 96 well plate ELISA)-other reasons

25

o PD participants who dropped out after baseline had significantly worse cognitive decline shown on HVLT (p=0.039), SDMT (p<0.001), LNS (p=0.031) and in BJLOT (p=0.002).

o Prodromal hyposmic participants with baseline data only had greater cognitive decline (by HVLT; p=0.0007, by SDMT, p=0.045) and lower mean caudate striatal binding ration (SBR) values (p=0.011) on DaTscan.

o iRBD participants with milder iRBD by RBDSQ (<6) were more likely to drop out after baseline assessment (p=0.029).

SECOND LONGITUDINAL ANALYSIS OF CSF ΑLPHA-SYNUCLEIN IN PPMI (MEASUREMENTS OF 2016)

26

Mollenhauer et al., Neurology under review

PPMI: CSF ΑLPHA-SYNUCLEIN RE-ANALYSIS OF 2016 BY ELISA IN(1) PD AND HEALTHY SUBJECTS UNTIL 36 MONTHS FOLLOW-UP(2) IN PRODROMAL SUBJECTS UNTIL 12 MONTHS FOLLOW-UP

1. CSF α-syn levels in PD were significantly lower in across all visits

2. CSF total α-syn levels in PD decreased slightly (p=0.031); levels showed a trend to increase in the control group (p=0.054).

3. Prodromals: The hyposmic participants showed the lowest mean CSF α-syn levels, while iRBD participants had intermediate levels.CSF α-syn remained relatively stable over time.

3. Changes in CSF α-syn were not related to changes in MDS-UPDRS III, MoCA and DaTscan values (p>0.05) in PD

4. We again found a longitudinal relationship between CSF α-syn and LED based on dopamine replacement (p=0.023) (not just dopamine agonists, p=0.145)

5. No association with genetic variants and SNCA transcripts

27

QUESTIONS?

Two and Four Year Longitudinal Assessment of DAT Imaging Biomarkers in a Progressing Parkinson Disease Cohort: Implications for Clinical Trial Design

John P Seibyl, MD on behalf of the PPMI Investigators

Institute for Neurodegenerative Disorders, and Invicro, New Haven, United States

411

PPMI Annual Meeting 2018Data Blitz

Measuring DAT changes in de novo PD with 123-I Ioflupane SPECT over four years

RATIONALE: Prior studies show loss of striatal signal Parkinson's patients studied longitudinally with 123-I Ioflupane SPECT. These studies demonstrate annual loss approximately 7 to 10% of SBR per year, but with significant between subject variance. The purpose of the present investigation was to evaluate different analytic approaches in a large PD cohort studied over four years with serial DAT SPECT.

RESEARCH QUESTIONS:1. Do analytic strategies incorporating curve fitting applied to serial

within subject longitudinal SBR data increase the signal size and reduce the variance compared with standard baseline-follow-up SBR change measures?

2. What are the implications of scan analysis method on clinical therapeutic trial design and sample size estimates?

• 343 PD patients in PPMI had serial ioflupane SPECT scans at baseIine, 1, and 2 years post enrollment, 271 PD patients had an additional 4 year scan

• Employed small region of interest template previously described for developing regional specific binding ratios (SBR).

• Strategies to measure SBR change follows two approaches.

METHODS

Method 1 Standard analysisDelta SBR= - SBR(y0)-SBR(y4)% change = delta SBR/SBR(y0)

Method 2 Exponential fitDelta and % change from equation

ANALYSIS METHODS

Years post Baseline Scan

Compare variance and signal:noise for each measure as well as strength of correlation with motor symptoms and power analyses for detecting change in clinical trials.

Mean Regional Percent Change SBR by Analysis Method

- 4 0

- 2 0

0

2 0

4 0

% c h a n g e b y r e g i o n / a n a l y s i s

% C

ha

ng

e S

BR

S t d 2 Y

S t d 1 Y

E x p 2 Y

n = 3 4 3 2 y

n = 2 7 1 4 y

i c c c i a p c a p i p c p i s c s m s

i = i p s i l a t e r a l

c = c o n t r a l a t e r a l

f o l l o w e d b y

c = c a u d a t e

a p = a n t . p u t a m e n

p = p u t a m e n

s = s t r i a t u m

m s = m e a n s t r i a t u m

S t d 1 Y S D

S t d 2 Y S D

E x p 2 Y S D

S t d 4 Y

E x p 4 y

S t d 4 y s d

E x p 4 Y s d

MeansSD

Sample Size Estimates by Striatal Region, Trial Duration, and Analysis Method

Observations:- Exp analysis better than

std- % change better than

delta SBR at 2,4 y, but not 1 y

- Ipsi better than contra- Contra put worst

I PS

I CA

UD

CO

NT

RA

CA

UD

I PS

I A P

CO

NT

RA

AP

I PS

I PU

T

CO

NT

RA

PU

T

I PS

I ST

RI A

CO

NT

RA

ST

RI A

ME

AN

ST

RI A

0

1 0 0

2 0 0

3 0 0

4 0 0

5 0 0

2 0 0 04 0 0 06 0 0 08 0 0 0

1 0 0 0 0

S a m p l e s i z e b y r e g i o n

Sa

mp

le

s

iz

e

S t d 1 y d

S t d 1 y %

S t d 2 y d

S t d 2 y %

S t d 4 y d

E x p 2 y d

E x p 4 y d

S t d 4 y %

E x p 2 y %

E x p 4 y %

A S S U M E S : 5 0 % e f f e c t s i z e

8 0 % p o w e r , p < 0 . 0 5 , 2 - t a i l e d

St d

1 y

d

St d

1 y

%

St d

2 y

d

St d

2 y

%

St d

4 y

d

St d

4 y

%

Ex

p2

y d

Ex

p2

y %

Ex

p 4

y d

Ex

p 4

y %

VM

AT

2Y

0

5 0

1 0 0

1 5 0

2 0 0

P e r c e n t 2 Y s a m p l e s i z e%

2

y

S

am

pl

e

Best Sample Size Estimate by Method, Normalized to 2 Y Standard Analysis

Delta SBR are green% Change SBR are blueBest region used

N=17

- 5 0 5 1 0

1

2

3

4

M e a n c u r v e s ( n = 2 7 1 , 4 Y d a t a )

Y E A R SS

BR

I P C A

C C A

I P A P

C A P

I P P

C P

S h i f t ( y )

0

1 . 5

3 . 0

6 . 0

7 . 0

1 0 . 5

Regional striatal SBR based on based on monexponential curve fits of 4 year, 4 scan data extrapolating 6 y prior and 11 y post baseline shows similar curves apparently phase shifted

Shifting the curves back to right such that they overlap results in an estimate of the years that region is ahead in the neurodegenerative process relative to ipsilateral caudate.

- 5 0 5 1 0

1

2

3

4

M e a n c u r v e s ( n = 2 7 1 , 4 Y d a t a )

Y E A R S

SB

R

I P C A

C C A

I P A P

C A P

I P P

C P

Conclusions

1. Exponential curve fitting analysis of serial longitudinal DAT SPECT results in similar change in SBR signal as the standard analysis, but with much less noise.

2. This permits sample sizes for the exponential analysis to be roughly half that of the standard analysis for comparable power for detecting a 50% slowing in rate of decline in a treatment v placebo cohort.

3. Different striatal sub-regions show better (ipsi striatum, ipsi ant putamen, mean striatum) or worse (contra putamen) power based on their signal to noise characteristics.

4. The different outcome measures, delta SBR v % change SBR, provide comparable power for the standard analysis, but the % change outcome is more robust with the exponential method.

5. Preliminary VMAT PET in a limited n shows great promise.6. Finally, speculative extrapolation of mean striatal subregion exponential

curves suggest a 10.5 year “head start” in SBR signal change for contra putamen over ipsi caudate.

37

QUESTIONS?

INNOVATIVE STATISTICAL DESIGN & ANALYSIS IN PD

Christopher S. CoffeyDepartment of Biostatistics

University of Iowa

May 02, 2018

The traditional approach to clinical trials tends to be large, costly, and time-consuming.There is a need for more efficient clinical trial design, which should lead to an increased chance of a “successful” trial that answers the question of interest.Hence, there is increasing interest in innovative trial designs.For example, adaptive designs allow reviewing accumulating information during an ongoing clinical trial to possibly modify trial characteristics.

OVERVIEW

39

An adaptive enrichment design fulfills the desire to target therapies to patients who can benefit the most from treatment.The first study period reveals participant groups most likely to benefit from treatment (discovery phase).Subgroup members are then randomized to treatment groups (validation phase).Hence, study power is increased (sample size decreased) by focusing only on subgroups most likely to show benefit.

ENRICHMENT DESIGNS

40

A sample size re-estimation (SSR) design refers to an adaptive design that allows for adjustment of sample size based on a review of the interim data.An internal pilot (IP) design refers to an SSR used to reassess nuisance parameters (only) mid-study.With moderate to large sample sizes, IP designs can be used to make appropriate modifications with minimal (if any) inflation of type I error rate.Thus, there is little reason (statistically) not to do this for most clinical trials!

SAMPLE SIZE RE-ESTIMATION

41

TIMEMean (SD)

Change From Baseline Power

Reduce by 50%

Reduce by 25%

All 7.5 (11.6) 80%90%

310410

12501650

Treated 5.0 (12.0) 80%90%

720960

28503800

Untreated 10.0 (10.6) 80%90%

150200

570760

Hypothetical Early PD Clinical Trial:• Required sample size for detecting change in MDS-

UPDRS OFF scores over 1 year (using PPMI Data)

MDS-UPDRS TOTAL – SAMPLE SIZE

42

Hypothetical Clinical Trial: Power greatly impacted by frequency of subjects

who start therapy within the 1 year period Can base sample size calculation on ‘best guess’ Two adaptive options:

• Enrichment Design: Identify those unlikely to require treatment during first year

• Internal Pilot Design: After some fraction of subjects have completed one year of

follow-up, estimate the percentage starting therapy If different from original assumption, re-estimate sample size

based on revised estimate

MDS-UPDRS TOTAL – SAMPLE SIZE

43

44

QUESTIONS?

THE FUTURE OF CLINICAL TRIALS

CENTER FOR HEALTH + TECHNOLOGYDO THE UNPRECEDENTED

Working to enable anyone, anywhere to receive care, participate in research, and benefit from the resulting advances.

Karl Kieburtz

PPMI PD DRUG VISUALIZATION

Predictive Disease Modeling + Simulation

Work by Monica Javidnia, Chris Snyder, Charles Venuto

PPMI PD DRUG VISUALIZATION (PD-RX)

47

PPMI PD DRUG VISUALIZATION (PD-RX)

Work by Monica Javidnia, Chris Snyder, Charles Venuto

Levodopa/Carbidopa (28%)Levodopa/Carbidopa dose adjustment (15%)Rasagiline (1.5%)

48

PREDICTING DISEASE PROGRESSION

Random Forest models constructed from PPMI to predict future MDS-UPDRS Part III scores

Work by Sam Lerman, Charles Venuto

49

EMERALD PDLeveraging Technology to Advance Care + Research

IN COLLABORATION WITH MIT, WE ARE NOW USING CELLULAR WAVES TO MONITOR GAIT AND MOVEMENT

Emerald-PD Overview:• We enrolled 7 participants with PD• Participants’ homes were fitted

with the Emerald device and observed for 8 weeks

• Aims of the study:• Assess ability of device to

measure disease features in the home

• Develop algorithms to characterize abnormal movements

• Assess response to medication

• Detect and quantify previously unmeasured symptoms

• Measure gait, sleep, and bathroom usageSource: http://www.emeraldforhome.com/

51

THE EMERALD DEVICE CAPTURES NOVEL DISEASE FEATURES IN REAL TIME

Source: Tarolli et al., in development (confidential)

Confidential

52

WE ARE DISCOVERING TRENDS IN SLEEP IN PD THAT WERE NOT PREVIOUSLY KNOWN

Midnight

Noon

Blue = time in bed

Red = large movements

53

54

QUESTIONS?

Baseline Predictors of Disease Progression by Andrew Siderowf

LIST OF BASELINE PREDICTORS AND OUTCOMES

Outcome Baseline predictor

Gender(male) UPSIT SCOPA-

AUTClinical site ESS RBDSQ

Total on 0.0626 NS 0.0573 NS NS NS

Total off 0.0311 0.079 NS NS NS NS

Part 3 on NS NS NS 0.0174 0.0833 NS

Part 3 off NS NS NS 0.0246 NS NS

Mean striatum NS NS NS NS NS <0.0001

Mean putamen NS NS NS NS NS 0.0039

SIGNIFICANT CLINICAL PREDICTORS ACROSS MODELS

Outcome Baseline predictorabeta mean striatum

Total on 0.0461 NS

Total off 0.0153 0.0733

Part 3 on 0.0417 NS

Part 3 off 0.0284 0.0469

Mean striatum 0.0531 NA

Mean putamen 0.06393 0.0662

SIGNIFICANT BIOMARKER PREDICTORS ACROSS MODELS

CHANGE IN STRIATAL DAT AND OFF UPDRS AS A FUNCTION OF BASELINE ABETA

-100

-50

050

perc

hg_s

triat

um

0 500 1000 1500 2000bl_abeta

-40

-20

020

4060

chg_

updr

s_to

tsco

re

0 500 1000 1500 2000bl_abeta

60

QUESTIONS?

THANK YOU

Documents

PARKINSON’S PROGRESSION MARKERS INITIATIVEPPMI Data BLITZ. Longitudinal . alpha-synuclein measurements in CSF. Brit Mollenhauer and Douglas Galasko, Biologics Working Group. 0 12