The Role of Digital Medicine in Autism Spectrum Disorder Clinical - PowerPoint PPT Presentation

The Role of Digital Medicine in Autism Spectrum Disorder Clinical Trials Gahan J. Pandina, PhD ISCTM-ECNP Joint Autumn Conference ▪ 6 September 2019 ▪ Copenhagen, Denmark

Disclosures • Full time employee of Janssen Research & Development LLC • Johnson & Johnson Stockholder • The opinions expressed in this presentation are those of Dr. Pandina, not Janssen or Johnson & Johnson 2

Agenda • Role of digital medicine in ASD clinical trials • Digital data collection – strengths and challenges • Sample sensor data / correlation with clinical results • Complex analytic approaches • Conclusions 3

Digital Medicine and ASD Clinical Trials Describes the intersection of autism assessment, diagnosis, and intervention and digital technologies Electronic health records Health-based video games Wearables and trackers Other Biosensors Web-and-mobile apps 4 Zhang et al (2018). Digital medicine: Emergence, definition, scope, and future. Digit Med, 4:1-4.

Digital Medicine and ASD Clinical Trials Describes the intersection of autism assessment, diagnosis, and intervention and digital technologies Sponsor CROs & Companies Partners Electronic health records Health-based video games INTEGRATION Academia, Regulatory Sites, Agencies Foundations, Wearables and trackers Other Biosensors Web-and-mobile apps 5 Zhang et al (2018). Digital medicine: Emergence, definition, scope, and future. Digit Med, 4:1-4.

Digital Medicine and ASD Clinical Trials Describes the intersection of autism assessment, diagnosis, and intervention and digital technologies Sponsor CROs & Companies Partners Electronic health records Health-based video games INTEGRATION Academia, Regulatory Sites, Agencies Foundations, Wearables and trackers Other Biosensors Biomarkers & Other Endpoints Web-and-mobile apps Population PoC Participant Change Selection Measurement Stratification 6 Zhang et al (2018). Digital medicine: Emergence, definition, scope, and future. Digit Med, 4:1-4.

Use of Digital Medicine in ASD Clinical Trials Strengths and Challenges Capture of digital endpoints – vs. standard paper rating scales Strengths Challenges Capture data in real time Cost of development, implementation, and maintenance • Hardware, software Central tracking Site and participant training, support, and account Reduced burden of data entry, scoring management Reduced data errors / loss Managing a compliant, global framework Optimize point-of-collection (clinical site, home, etc.) Back-up approach in case of problems Pre-programmed queries/skip- outs (no “maybe”) Full audit trail / data QC Maintain control over comments and writing in the margin Passive, “automatic” data capture 7 Zhang et al (2018). Digital medicine: Emergence, definition, scope, and future. Digit Med, 4:1-4.

Overview - Janssen Autism Knowledge Engine * ** *Please note that JAKE is for research use only 8 **Ness et al (2019). Frontiers Neurosci: 13:111. https://doi.org/10.3389/fnins.2019.00111

Autism Biomarker Study Design 14-day 8 or 10 Week Prospective Monitoring Screening Phase Single visit Key Inclusion/ Exclusion N=41: Typically Developing Controls • Individuals with a diagnosis of ASD BSWB, rating scales, single assessment • • Aged 6 years to adult • 9 US centers • IQ of 60+ 8-10 weeks • Ongoing behavioral and pharmacologic N=144 ASD Treatment therapies allowed No restrictions on concurrent therapies • • English speaking 3 site visits w/ BSWB (BL, Wk 4, Endpoint) • • Capable of participation in study procedures ASD TD N 144 41 Male le 112 (77.8) 27 (65.9) Mea ean Age e (SD (SD) 14.58 (7.83) 16.27 (13.176) 9 Ness et al (2019). Frontiers Neurosci: 13:111. https://doi.org/10.3389/fnins.2019.00111

Autism Behavior Inventory (ABI; v1.1) ABI 5 Symptom Domains Core Domains Restric icted Soc ocia ial l Com ommunic icatio ion Behavior or Associated Domains Moo ood & & Self lf Chall allenging Anx nxie iety Regula latio ion Behavior ors ABI / Other Scale Domain Correl. ( r ) ABI / Other Scale Domain Correl. ( r ) • 62 62-item scale le (mon (monthly ly) ABI Core --- SRS Total 0.81 ABI-MA --- CASI Anxiety 0.77 • 24 24-item Sh Short ort form orm (bi (bi-weekly ly) • Ra ABI-RRB --- RBSR Total 0.76 ABI-SR --- ABC Hyper, NonCom 0.88 Rated 0 to o 3 (0 (0 to o ma max.) ABI-RRB --- SRS RI, RB 0.75 ABI-SR --- ABC Inapp Speech 0.64 ABI-SC --- SRS SC, I 0.66 ABI-CB --- ABC Irritability 0.76 1 Bangerter et al (2019). J Autism Dev Disorders, online at https://doi.org/10.1007/s10803-019-03965-7 10 2 Data on file

Visual Exploration Test 11 11 Manyakov et al (2018). Autism Research 11: 1554-1566.

Visual Exploration Test – Broad Differences in ASD vs. TD Example: Exploration Participants with ASD (vs. TD) showed significantly: • Less exploration of all images Exploration (#/% valid time) • Greater perseveration on high autism interest items • Perseveration on HAI items in both social and non- social arrays P<0.001 12 12 Manyakov et al (2018). Autism Research 11: 1554-1566.

Visual Exploration Test – Broad Differences in ASD vs. TD Example: Exploration Participants with ASD (vs. TD) showed significantly: • Less exploration of all images Exploration (#/% valid time) • Greater perseveration on high autism interest items • Perseveration on HAI items in both social and non- social arrays BUT…. Visual processing is a complex behavioral phenomenon! P<0.001 More complex features may tell us more about how visual perceptual processing affects social behavior … does combining variables or constructing new features result in better as biomarkers ? 13 13 Manyakov et al (2018). Autism Research 11: 1554-1566.

Eye-Tracking Data May Benefit from More Complex Analytic Approach RQA (Recurrence Quantification Analysis) approach to identify eye-tracking patterns 14 14 Manyakov et al (2018). Autism Research 11: 1554-1566.

Eye-Tracking Data May Benefit from More Complex Analytic Approach RQA (Recurrence Quantification Analysis) approach to identify eye-tracking patterns Determinism Recurrence Rate Repetitive patterns of Patterns and duration of gaze shift refixations Pairs of consecutive RR only fixations Repeated shift RR and detail between images orientation Center of Recurrence Laminarity Mass Fixation and rescanning pattern Timing between fixations Single fixation, then Recurrent fixations rescanned in detail close in time Detailed first fixation, Recurrent fixations then quickly rescanned distant in time 15 15 Manyakov et al (2018). Autism Research 11: 1554-1566.

Eye-Tracking Data May Benefit from More Complex Analytic Approach RQA (Recurrence Quantification Analysis) approach to identify eye-tracking patterns Determinism Recurrence Rate Repetitive patterns of Patterns and duration of gaze shift refixations Pairs of consecutive RR only fixations Repeated shift RR and detail between images orientation Statistically lower in ASD vs. TD, correlates to symptoms Center of Recurrence Laminarity Mass Fixation and rescanning pattern Timing between fixations Single fixation, then Recurrent fixations rescanned in detail close in time Detailed first fixation, Recurrent fixations then quickly rescanned distant in time 16 16 Manyakov et al (2018). Autism Research 11: 1554-1566.

Funny Videos Do individuals with ASD have an atypical affective response? Measurement of spontaneous affective • response at time proximal to “event” May be more relevant to social • exchange than prompted affect Not perceived as “tests” • Appear to have durable response, even • with repetition 17 Bangerter et al. (submitted).

Funny Videos – FACET Results Differential spontaneous affective response in ASD subgroups • Wide variability in ASD affective response to funny videos • ASD – 2 subgroups emerge • “Over - responders” ( red ) Lip corner puller • “Under - responders” ( blue ) Cheek raiser 1 Ban angerter et al, al, in in pr preparation 18 18 Bangerter et al. (submitted).

Funny Videos – FACET Results Differential spontaneous affective response in ASD subgroups • Wide variability in ASD affective response to funny videos • ASD – 2 subgroups emerge • “Over - responders” ( red ) Lip corner puller • “Under - responders” ( blue ) Over-responders (vs TD group )* • AU6 avg: p <0.001, r = 0.62 • AU12 avg: p <0.001, r = 0.3 Cheek raiser Under-responders (vs. TD group )* • AU6 avg: p <0.001, r = - 0.25 • AU12 avg: p <0.001, r = - 0.36 *Linear regression (sex, age) 1 Ban angerter et al, al, in in pr preparation 19 19 Bangerter et al. (submitted).

Resting state EEG • Both eyes open (hourglass) and eyes closed conditions Janssen Research & Development Confidential 20 Janssen Research & Development – data in preparation

EEG Power Resting State, Eyes Open Results Hypothesis Wang et al. 2013 Significant difference is seen mainly in Alpha at posterior regions Preliminary results 21 Janssen Research & Development – data in preparation

EEG Power Resting State, Eyes Open Correlation between alpha power and symptom severity ( r ~ -.21) Preliminary results 22 Janssen Research & Development – data in preparation