)

EU-AIMS: 25 partners

2

1

Cellular

assays

2

Animal

models

3

Translational

science

4

Clinical

Research

5

Genomics

EU-AIMS

New evidence based treatments

Best performing IMI project – Reuters - 2017

Biosource and data

management

)

Integrated translational approaches

EU-AIMS

Loth, Spooren & Murphy, Lancet Psychiatry, 2014

Charman et al Mol Autism 2107, Loth et al Mol Autism, 2017

)

Clinical sites: London, Cambridge, Stockholm, Utrecht, Nijmegen,

Mannheim, Rome

Future biomarkers for patient stratificationRecruitment and protocol

GCP standardized

Charman et al Mol Autism 2107, Loth et al Mol Autism, 2017

)

First neuropsychiatric consortium that submitted core

protocol for QA on biomarker approaches & methodologies

• EMA endorsed our biomarker strategy (27 queries, 5 topics)

• EMA emphasized need for replication

• EMA emphasized need for objective outcome measures

• EMA emphasized need for demonstrating reliability + validity of basic cellular

and rodent models.

Building regulatory framework in EuropeClinical endpoints and biomarkers - LEAP

)

●

●

●

●

6

)

Replication

●ABC-CT

●Within EU AIMS

7

)

N170

8

)

N170: Strongest in children

9

)

Relations to behavior

10

)

Resting-state fMRI: Consistent alterations in functional

connectivity across four ASD cohorts > 2000 individuals

Holiga et al. in submission

Stefan Holiga,

Juergen Dukart

) 12

Resting-state networks in foetal and neonatal brain

at 30+/-4 gestational weeks.

Ciarrusta et al. JAMA online, in press 2019

)

GABA-A. No difference in receptor

binding. 2 centres. Human + rodent

13

Horder et al, Science Translational Medicine Oct 2018.

)

Reliability + validity

of basic cellular and

rodent models

15

)

From rodent to human, and back

Baudouin et al (2012) Science

Glx

/Ch

o r

atio

Glx

/Ch

o r

atio

Corrected age (weeks)

Control Atypical HFA or

Asperger

Human E/IASD individuals

At risk infants

a

E/I imbalance in a rodent model of ASD

• Increased mGluR1

expression

• Ectopic synapse formation

• Reduced glutamatergic

transmission

• Rescued by re-expression of

Neuroligin 3

Horder Transl Psychiatry 2013

)

C

*

* Drug x Group interaction: p= 0.038.

Paired analysis- RM ANOVA

control ASD

ASD vs Control

before modulation

ASD vs Control

After modulation

Can we ‘switch the dial’ ?

Differences in E/I

modulate

Functional

Connectivity

Control E/I ASD E/I

Arjam et al Transl Psychiatry. 2017

)

Impact of Arbaclofen 15 vs 30 mg

GABA

Arbaclofen 15mg Arbaclofen 30 mg

)

It’s the individuals that count !

19

)

30mg shift of right N170 amplitude and

frontal GABA

CTRL pearson r = 0.008, p = 0.982

ASD pearson r = 0.567, p = 0.112

For the amplitude a “increase” means more

negative

Increases with drug

Decreases with drug

FUTURE DIRECTIONSAIMS-2-TRIALS

Our Vision

To develop a precision medicine approach to Autism Spectrum Disorder that better matches treatments to patients by validating stratification biomarkers, testing novel or repurposed drugs in International clinical trials network, and identifying novel targets.

of brain structure8 and function9 and relate

them to clinical outcome. Neuroimaging

techniques also help us to identify the mecha-

nisms through which interventions improve

functioning. Recent event-related potential

and pharmacological fMRI10 studies have been

used to ascertain whether treatment effects

are reached because of the normalization

of atypical neural processes, or owing to the

development of compensatory mechanisms.

Last, molecular biomarkers will be crucial in

predicting treatment response. Network-based

stratification approaches have recently been

successfully used in cancer research to identify

tumour subtypes. We will use similar approaches

to identify molecular ASD subgroups on the

basis of the entire genetic mutation profile. We

then aim to map these molecular groups, from

the ‘bottom up’, to neurobiological biomarkers

and clinical symptom profiles.

Conclusions

The CHMP’s key recommendations on the

LEAP study design and biomarker approaches

included the need to establish sensitivity and

specificity for all candidate biomarkers and to

define cut-offs for quantitative stratification

markers. The CHMP also highlighted the need

to establish, for each candidate biomarker, how

abnormalities map on to differences in prog-

nosis, and to define what would be considered

Figure 1 | Examples of biomarker stratification approaches. a | For each

cognitive, neuroimaging and biochemical measure, the abnormalities of

each participant with autism spectrum disorder (ASD) are estimated

based on deviations from the typical development (TD) at a particular age.

In the left panel, the abnormality is only seen in a subgroup of people with

ASD at a particular developmental stage (for example, during middle

childhood). In the right panel, subgroups with opposing abnormalities (for

example, functional hyperconnectivity versus functional hypoconnectiv-

ity in particular networks) are identified that persist across different ages.

Both scenarios are hypothetical only. Ovals represent the establishment

of different (distinct) subgroups. b | Patient stratification according to

neuroimaging biomarkers that combine different indices of, for example,

brain structure and connectivity, as derived from structural magnetic reso-

nance imaging (sMRI), functional MRI (fMRI) and diffusion tensor imaging

(DTI). Different subgroups are then mapped on to potentially shared ver-

sus potentially distinct clinical profiles. For example, clinical profile A

may be characterized by particular sensory abnormalities, whereas clini-

cal profile B may be characterized by a pattern of particular social-

communicative deficits. c | Subgroups can be identified according to

differences in their genetic–molecular profile. These stratified groups can

then be compared with one another in terms of biochemical biomarkers,

brain structure and function, cognition and clinical profile.

Nature Reviews | Drug Discovery

• Biochemical biomarkers• Brain structure and function

Comparisons of:

Genetic ormolecularpro lin

• o niti e pro le• linical pro le

c

• sMRI• fMRI• DTI

trati cation accordin toneuroima in biomar ers

b

a

linical pro le linical pro le B linical pro le

Para

mete

r

e

TD mean

indi idual

Para

mete

r

e

CORRESPONDENCE

NATURE REVIEWS | DRUG DISCOVERY www.nature.com/nrd

© 2016 Macmillan Publishers Limited. All rights reserved

of brain structure8 and function9 and relate

them to clinical outcome. Neuroimaging

techniques also help us to identify the mecha-

nisms through which interventions improve

functioning. Recent event-related potential

and pharmacological fMRI10 studies have been

used to ascertain whether treatment effects

are reached because of the normalization

of atypical neural processes, or owing to the

development of compensatory mechanisms.

Last, molecular biomarkers will be crucial in

predicting treatment response. Network-based

stratification approaches have recently been

successfully used in cancer research to identify

tumour subtypes. We will use similar approaches

to identify molecular ASD subgroups on the

basis of the entire genetic mutation profile. We

then aim to map these molecular groups, from

the ‘bottom up’, to neurobiological biomarkers

and clinical symptom profiles.

Conclusions

The CHMP’s key recommendations on the

LEAP study design and biomarker approaches

included the need to establish sensitivity and

specificity for all candidate biomarkers and to

define cut-offs for quantitative stratification

markers. The CHMP also highlighted the need

to establish, for each candidate biomarker, how

abnormalities map on to differences in prog-

nosis, and to define what would be considered

Figure 1 | Examples of biomarker stratification approaches. a | For each

cognitive, neuroimaging and biochemical measure, the abnormalities of

each participant with autism spectrum disorder (ASD) are estimated

based on deviations from the typical development (TD) at a particular age.

In the left panel, the abnormality is only seen in a subgroup of people with

ASD at a particular developmental stage (for example, during middle

childhood). In the right panel, subgroups with opposing abnormalities (for

example, functional hyperconnectivity versus functional hypoconnectiv-

ity in particular networks) are identified that persist across different ages.

Both scenarios are hypothetical only. Ovals represent the establishment

of different (distinct) subgroups. b | Patient stratification according to

neuroimaging biomarkers that combine different indices of, for example,

brain structure and connectivity, as derived from structural magnetic reso-

nance imaging (sMRI), functional MRI (fMRI) and diffusion tensor imaging

(DTI). Different subgroups are then mapped on to potentially shared ver-

sus potentially distinct clinical profiles. For example, clinical profile A

may be characterized by particular sensory abnormalities, whereas clini-

cal profile B may be characterized by a pattern of particular social-

communicative deficits. c | Subgroups can be identified according to

differences in their genetic–molecular profile. These stratified groups can

then be compared with one another in terms of biochemical biomarkers,

brain structure and function, cognition and clinical profile.

Nature Reviews | Drug Discovery

• Biochemical biomarkers• Brain structure and function

Comparisons of:

Genetic ormolecularpro lin

• o niti e pro le• linical pro le

c

• sMRI• fMRI• DTI

trati cation accordin toneuroima in biomar ers

b

a

linical pro le linical pro le B linical pro le

Para

mete

r

e

TD mean

indi idual

Para

mete

r

e

CORRESPONDENCE

NATURE REVIEWS | DRUG DISCOVERY www.nature.com/nrd

© 2016 Macmillan Publishers Limited. All rights reserved

HR Fetus

HR infants Preschoolers Children to adults + epilepsy

SHANK3'and'NRXN1'cohorts!

New$$ New$$

3rd

trimester 4 mths 3 yrs 6 yrs 12 yrs 30 yrs

New$$

New$$

Pups Juveniles Adults

3rd

trimester

iPSCs$

Inflammatory$$

astrocytes$

microglia$

AIMS-2-TRIALS

AIMS-2-TRIALSDeveloping a clinical trials network + running trials

KCL

APHP

RUNMC)

SERMAS

AIMS-2-TRIALS Populations

7,500 patients +

=PMS and NRXN1 network

USAL+

FSM+

FCRB+

UC+

FPGF+

INSERM+

UGENT+

CHUT+

UULM+

KI+

UMCU+

GU+

CIMH+

RG+

TCD+

UGLA+

UEDIN+ UNE

W+

UCAM+

Canada

POND

USA

SFARI

ATN

ABC-CT

Contrived tasksPro: interpretably quantifiable, greater experimental control

Con: artificial, oversimplified, incomplete coverage

Explicit rule-based simulated interactions

Underlying processessupporting sociality

Implicit norm-based social interactions

Explicit rule-based social interactions

Real social interactionsPro: ecologically valid, clinically relevant;

Con: intractably multifactorial, requires multiple people (burden)

The promise of digital biomarkers for ASD

24

Quantifiable physiological and behavioural data collected by digital devices

May support sensitive, ecologically valid, individualisedendpoints

Many with ASD have an affinity for new technology

Symptoms may be amenable to digital measurement

Minimising individual burden Minimising technical footprint2–3 devices, practical in real world

Anchored to clinical scalesVineland II, PSQI, CGI

• Active tests allow for remote testing in home environments• Passive monitoring can occur unobtrusively, continuously• Individual/caregiver reports

What symptoms matter most to individuals? Measure those.

Input from individuals with autism, support people & advocates

• Our starting place: What matters?

– Concept development began Q2 2017

– Enumerating the symptoms amenable to digital measurement from a conceptual model of ASD

• Input from clinical experts & advocates:

– Consultations throughout initial concept development

– 2018 Advocacy Group Advisory Board

– INSAR 2018 Advisory Board

Published in: Fiona McDougall; Thomas Willgoss; Steve Hwang; Federico

Bolognani; Lorraine Murtagh; Evdokia Anagnostou; Diana Rofail;

Autism 22, 953-969.

DOI: 10.1177/1362361317718987

Copyright © 2017 The National Autistic Society, SAGE Publications

Conceptual model of the impact of living with ASD.

Participatory Design

Tested and refined with user experience focus sessions

• Almost all participants would be willing to do the tasks in the context of a clinical trial

• Smartwatch was well received, in terms of design and comfort

• Almost all participants were open to using Beacons in their home, without privacy or feasibility concerns

• Only minor usability issues were observed, which were addressed with modifications to active tasks

• Feasibility of performing tasks depended on age and IQ –added option for healthcare professional to deactivate task

UKLondonManchester

USAAtlantaLos Angeles

Objectives Test acceptance and feasibility; user experience; age and IQ appropriateness

Participants n=23, 18-45yrs

n=38, 5-45yrs6 age and IQ groups

Format 1:2 Interview (Moderator:Individual with ASD, Support Person)

Design of Focus Sessions

SURVEYS

Daily Diary Sleep Diary EQ-5D-5L

A Digital Platform for Arbaclofen: “My ASD” app

Passive monitoring, <5 mins of active time per day, & a conversation once per week

PASSIVE MONITORING

BeaconsSociability

Body MotionRepetitive Movements

PPGAnxiety

Ambient SoundCommunication

ACTIVE TASKS

Find the EggSpatial Working Memory

Facial ExpressionsEmotion Recognition

Treasure HuntCooperation

Describe the Picture

Vocal Features

ConversationCommunication

“I would love for you to have this,” says study partner.

“Yeah, I would like to have the watch!” – participant

LA01

“Very interesting work; glad someone’s doing it.” – study partner of AT17

“Oh I think it’s terrific.” –

study partner of LA09

“This is the first time I’ve heard of anything like this. This would be really great” – study partner of AT20

“Some very cool stuff!” – Thomas Frazier,

Chief Scientific Officer of ”Autism Speaks”

Feedback from individuals, caregivers and advocates

28

n≈60 across 3 focus groups in UK and USA

Individuals w/ASD: Study partners of those with ASD:

Advocacy groups:

Ongoing deployment efforts:

• n=90 longitudinal observational study

• n=100 substudy deployment in balovaptan trial

Upcoming deployments:

• AIMS-2-TRIALS Arbaclofen Study (IMI2 project)

Thank you to all participants

)

Eyetracking: natural scenes

30

)

Dynamic scenes: Looking time

o NT group looked more at eyes

and faces than ASD group, who

looked more at bodies and

background

aoi: BG_PEOPLE

ASD NT

diag

0.08

0.1

0.12

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

bab

ility

aoi: BODY

ASD NT

diag

0.06

0.08

0.1

0.12

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

ba

bili

ty

aoi: EYES

ASD NT

diag

0.08

0.1

0.12

0.14

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

ba

bili

ty

aoi: FACE

ASD NT

diag

0.4

0.5

ratio

InA

OI_

na

nm

ea

n

0.2 0.4 0.6 0.8 1

ratioInAOI_nanmean

Pro

bab

ility

aoi: MOUTH

ASD NT

diag

0.1

0.12

0.14

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

ba

bili

ty

aoi: BG_PEOPLE

ASD NT

diag

0.08

0.1

0.12

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

bab

ility

aoi: BODY

ASD NT

diag

0.06

0.08

0.1

0.12

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

ba

bili

ty

aoi: EYES

ASD NT

diag

0.08

0.1

0.12

0.14

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

ba

bili

ty

aoi: FACE

ASD NT

diag

0.4

0.5

ratio

InA

OI_

na

nm

ea

n

0.2 0.4 0.6 0.8 1

ratioInAOI_nanmean

Pro

bab

ility

aoi: MOUTH

ASD NT

diag

0.1

0.12

0.14

ratio

InA

OI_

na

nm

ea

n

0 0.2 0.4

ratioInAOI_nanmean

Pro

ba

bili

ty

Background People (p=.001)

Bodies (p<.001)

Eyes (p=0.043)

Faces (p<.001)

Mouths (p=.554)

)

aoi: BG_PEOPLE

ASD NT

diag

1.5

2

2.5

pe

akL

oo

k_

na

nm

ea

n

0 5 10

peakLook_nanmean

Pro

ba

bili

ty

aoi: BODY

ASD NT

diag

0.8

1

pe

akL

oo

k_

na

nm

ea

n

1 2 3

peakLook_nanmean

Pro

bab

ility

aoi: EYES

ASD NT

diag

0.8

1

1.2

pe

akL

oo

k_

nan

mea

n

1 2 3

peakLook_nanmean

Pro

ba

bili

ty

aoi: FACE

ASD NT

diag

3

4

5

pe

akL

oo

k_

na

nm

ea

n

0 5 10 15

peakLook_nanmean

Pro

ba

bili

ty

aoi: MOUTH

ASD NT

diag

1

1.2

1.4

pe

akL

oo

k_

na

nm

ea

n1 2 3 4

peakLook_nanmean

Pro

bab

ility

aoi: BG_PEOPLE

ASD NT

diag

1.5

2

2.5

pe

akL

oo

k_

na

nm

ea

n

0 5 10

peakLook_nanmean

Pro

ba

bili

ty

aoi: BODY

ASD NT

diag

0.8

1

pe

akL

oo

k_

na

nm

ea

n

1 2 3

peakLook_nanmean

Pro

bab

ility

aoi: EYES

ASD NT

diag

0.8

1

1.2

pe

akL

oo

k_

nan

mea

n

1 2 3

peakLook_nanmean

Pro

ba

bili

ty

aoi: FACE

ASD NT

diag

3

4

5

pe

akL

oo

k_

na

nm

ea

n

0 5 10 15

peakLook_nanmean

Pro

ba

bili

ty

aoi: MOUTH

ASD NT

diag

1

1.2

1.4

pe

akL

oo

k_

na

nm

ea

n

1 2 3 4

peakLook_nanmean

Pro

bab

ility

Dynamic scenes: Peak look

o ASD group exhibited shorter

peak look durations than NT

group to eyes and faces

Background People (p=.082)

Bodies (p=.163)

Eyes (p=.005)

Faces (p<.001)

Mouths (p=.567)

AIMS-2-TRIALS

● More details on stratification biomarker analyses, including time-line of work

Validation stratification markers

Biomarker discovery – Multimodal fingerprints

Fetal recruitment

Validation multimodal fingerprints

First wave @ 3 yrs

Second wave @ 4- 4.5 yrs

Third wave @ 5.5- 6 yrs

Discovery prognostic biomarker Third assessment wave

Postnatal follow up: neonate, 4, 10, 14, 24, 36 months

Fig 12.

Discovery prognostic biomarker

Discovery “synaptic markers”

Assessment PMS/ NRXN1

Validation synaptic biomarker

V alidation risk stratification biomarkers

1 month 12 24 36 48 60

Discovery risk stratification biomarkers

SPS follow-up

Cohorts

LEAP

N=730

PIP N=400

SynaG

N=100

HR

fetal/ infant

N=150

Validation synaptic biomarker