EU-AIMS: 25 partners - ISCTMIntegrated translational approaches EU-AIMS. Loth, Spooren & Murphy,...
Transcript of EU-AIMS: 25 partners - ISCTMIntegrated translational approaches EU-AIMS. Loth, Spooren & Murphy,...
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EU-AIMS: 25 partners
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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
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Integrated translational approaches
EU-AIMS
Loth, Spooren & Murphy, Lancet Psychiatry, 2014
Charman et al Mol Autism 2107, Loth et al Mol Autism, 2017
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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
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●
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Replication
●ABC-CT
●Within EU AIMS
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)
N170
8
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N170: Strongest in children
9
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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
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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
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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
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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