Presentation by Boaz Hirshberg, VP, Clinical Development, Cardiovascular, Renal, Metabolic Disease at AstraZeneca - The Promise of Digital Technology in Drug Development Clinical Trials. Includes the following: - The vision for patient-centric medical care delivery - End-to-end patient experience enhanced by digital technologies - Digital technologies have a potential to transform clinical trial & medical care delivery - Example: transforming our understanding of Type 2 diabetes with remote patient monitoring - Frequent sampling demonstrates glucose lowering very soon after first dose, which might be unappreciated in typical trial design - Multiple data points reduce uncertainty about the glucose outcome and enable future machine learning of unanticipated relationships - Lessons learned from CGM pilot: data storage, transfer, and analysis - Defining the clinical science questions to be answered - Operational considerations for incorporating digital data into clinical development - Addressing challenges of digital technologies’ disruption

1. The Promise of Digital Technology in
Drug Development Clinical Trials
Boaz Hirshberg MD
VP, Clinical Development, Cardiovascular, Renal, Metabolic Disease, AstraZeneca
mHealth Israel, May 15, 2019 in Tel Aviv, Israel 15 May 2019

2. The vision for patient-centric medical care delivery
2
CURRENT: Linear, limited patient/physician interactions
FUTURE: De-centralized
medical care delivery at
patient’s home with feedback
loop between physician and
patient for adaptive treatments
Enrolled in
the study
Receives
treatment
Patient Clinic Physician
Safety
and efficacy
monitoring
Real-time
data
visualization
Feedback
reminders
Machine
Learning/A.I.

3. End-to-end patient experience enhanced by digital
technologies
3
Screening During trial
Find trial
Simplify
consent process
First
engagement
Collect
data
Improve adherence
and outcomes
Activating patients
and connecting them
to trials through
• Social Media
• Paid Search
• EMR audit
Electronic consent:
Interactive and
adaptive to patient
questions trial guide,
includes screening
criteria
Guided visit tool for
site and patient:
utilising mixed media
training, smart phone
or voice activated
device enabled
Data from the
source through
medical devices to
enable immediate
access by patient
and physician for
improved care
Behavioural
science platform /
Digital Therapeutics
to enable better
adherence and
data-rich clinical
intervention
E N AB L E D B Y D AT A AN D AN AL Y T I C S P L AT F O R M

4. Digital technologies have a potential to
transform clinical trial & medical care delivery
4
Simplify
Logistics
1
Improve
Patient
Engagement
2
Generate
New
Science
3

5. Illustrative example: transforming our understanding of
Type 2 diabetes with remote patient monitoring
5

6. Frequent sampling demonstrates glucose lowering very
soon after first dose, which might be unappreciated in
typical trial design
6
Red arrows denote early post-prandial (Day 1)
and fasting glucose (Day 5) reduction

7. Multiple data points reduce uncertainty about the glucose
outcome and enable future machine learning of
unanticipated relationships
7
Time of day
bpmnmol/L

8. Frequent sampling enables endpoints not previously
assessed in type 2 diabetic patients
8
Hyperglycaemia
>140 mg/dl
Normoglycaemia
Hypoglycaemia
<54 mg/dl
Placebo Treated
Time spent in target glycaemic range on day 49
Patients consider time in range the most important predictor of quality of life.
How does it correlate with diabetes complications?

9. CGM highly correlated with validated plasma glucose
results. Reduced variability enables signal detection with
fewer patients
9
50
70
90
110
130
150
170
190
210
230
80% 85% 90%
Sample size by different endpoints
Sample Size A1c Sample Size Average Glucose
R = 0.9, p,0.001
*
AUC: area under the two-hour blood glucose response curve
#ofpatients

10. Direct patient data will drive change
10
Minimize site
visits
Mechanistic
understanding
Disease discovery
Faster data
collection
Fewer data errors
Increase access to
trials for all patients
Reduce patient
burden
Simplify
Logistics
Generate
New Science
Improve Patient
Engagement

11. Lessons learned from CGM pilot: selecting the right device
for patients
11
SatisfactionUsability testing Adherence
Patient
Experimental
Placebo
Days into trial

12. Lessons learned from CGM pilot: data storage, transfer,
and analysis
12
Managing device, imaging, and mobile apps data requires
understanding of:
• Purpose
• Data transfer
• Data security
• Data format
Micro-
services
Secure
data
Cloud
hosting
Machine
learning

13. Defining the clinical science questions to be answered
13
• Pre-plan data use
• Establish rules for return of data to patients and
caregivers
• Perform clinical validation of digital endpoints
• Develop rigorous new study designs for adaptive
trials using real-time data from the source,
delivering further improvements in patient health
+
Doing something
completely different…

14. Operational considerations for incorporating digital data
into clinical development
14
BIG DATA NEEDS CONSIDERATIONS
Clinical
imaging
Data Access
FAIR-compliant data repository
Customized metadata model, robust
infrastructure
Data Analysis
Imaging ML/AI platforms
DL methodology development
Wearables &
mobile apps
Data Access
Data acquisition and storage capabilities
Comprehensive digital data platform
Data Analysis
Statistical analysis and signal processing to
create AI/ML models
Prognostic and predictive digital markers

15. Addressing challenges of digital technologies’ disruption
15
• Better “human use” assessments of the right devices
• Simplified data transfer solutions
• Standardized data elements
• Flexible guidelines for defining fit for purpose devices
• Patient facing applications and visualization tools that can
be used across devices
• Trial and statistical methods to incorporate adaptive
designs
Solutions will require cross-functional partnerships