As the adoption of remote monitoring, wearable devices and mobile applications grows, digital biomarkers will play a significant role in better disease identification and health management.

1. February 2021
Evolving Role of
Digital Biomarkers
in Healthcare
White Paper

2. The impact of technology intervention in the
healthcare industry is inevitable and its application has
led to improved patient outcomes from drug
development to drug optimization (post-approval).
Digital biomarkers enable continuous data collection,
empowering healthcare professionals to make data-
driven decisions.
Insights via Digital Biomarker
▪ Sensors
▪ Data Processing and Analysis
▪ Disease Insights
In 1998, the National Institutes of Health Biomarkers
defined biomarker as “a characteristic that is
objectively measured and evaluated as an indicator
of normal biological processes, pathogenic
processes, or pharmacologic responses to a
therapeutic intervention.”
Biomarkers play a critical role in understanding how
changes in biological processes affect clinical
outcomes. It helps in understanding the relationship
between measurable biological processes (e.g., pulse
rate, blood pressure, etc.) and its clinical outcome due
to therapeutic interventions, which help in better drug
development, enhancement of drug effectiveness, etc.
Traditional methods lack the ability to continuously
monitor biological processes and collect relevant data
points for effective treatment of diseases. The advent
of digital biomarkers has made it possible to map and
leverage hundreds of data points for delivering better
treatment.
For instance, monitoring and analysis of sleep patterns
help doctors diagnose early stages of dementia and
provide appropriate recommendation for sleep
changes.
The contextual map of biomarkers draws all possible
parameters that affect biological and pathogenic
processes, or pharmacologic responses to a
therapeutic intervention or the intrinsic progression of
disease.
INTRODUCTION
1

3. Digital biomarkers have evolved from smart devices
that track an individual’s basic health vitals to playing a
major role in diagnosis, screening, and treatment of
patients. For e.g., while treating a dementia patient,
sleep monitoring devises with digital biomarkers can
help in obtaining insights on patient responses to
treatment and disease progression.
Earlysense Live, an under-mattress sleep and health
tracker, monitors heart rate, respiratory rate, and
motion to provide real-time data. It monitors sleep
quality, awake time, deep sleep, REM phase, and stress
levels. Frequent measures capture intraindividual
variability in sleep patterns that act as indicators of
change and thus detect subtle health transitions (e.g.,
Healthy to Mild Cognitive Impairment).
A transformative approach can also lead to discovery
of novel and innovative digital indicators such as gait-
speed variability, metadata usage, etc.
Similarly, data can be captured for other factors (see
figure 1) through digital biomarkers based on disease
of interest and monitoring of patient health.
Currently, there are multiple digital biomarkers under
development and are categorized based on its
functionality.
2
DIGITAL BIOMARKERS: REAL WORLD APPLICATIONS (1/2)
Fig. 1 – Factors Influencing Digital
Biomarkers Development

4. 3
DIGITAL BIOMARKERS: REAL WORLD APPLICATIONS (2/2)
Table 1 – Types of Digital Biomarkers
Biomarker Function Application
Susceptibility /
Risk
Identify potential for disease which does
not exhibit any clinically apparent medical
condition
Detect cognitive changes in healthy
subjects at risk of developing Alzheimer's
disease using a video game platform
Diagnostic Confirm the presence of disease Multiple blood pressure reading
Monitoring Assess the status of medical condition Monitor signs of change in disease (e.g.,
Parkinson's) using smartphone-based
measurements
Prognostic Identify the disease recurrence or
progression of disease
Stratify mental health conditions and
predict remission using passively collected
data from smartphone

5. 4
DIGITAL BIOMARKERS: ENHANCING DRUG EFFECTIVENESS
1. Identify Recurrence of Disease & Improve Drug
Effectiveness
Drug effectiveness refers to the extent at which a drug
achieves its intended effect in a typical clinical setting.
In certain cases, some life-threatening diseases such as
cancer have chances of recurrence post surgery.
It is arduous to identify and pinpoint the exact location
and growth of tumor cells and the onset of cancer.
However, prognostic biomarkers can provide data by
capturing body weight which is indicative of
recurrence of cancer post surgery.
This information helps in modifying the prescribed
medication by adding other medication(s), changing
dosage to increase its effectiveness.
2. Improve Drug Effectiveness
In general, drug dosage timing is prescribed in vague
language (morning, afternoon and evening). However,
via monitoring biomarkers patients can be studied and
drug timings can be prescribed precisely.
Diabetic patients are usually advised to take insulin
shots 30 minutes before eating in order to keep their
sugar levels normal. However, studies suggest 20
minutes is the optimum time to administer insulin and
is more effective than the customary half-hour.
Every individual need is unique and monitoring
biomarkers help in prescribing medication precisely for
enhancing the effectiveness of drugs.
Biomarker Disease Benefit
Prognostic Tumor Cells ▪ Increase drug
effectiveness
▪ Treatment at initial
stages of recurrence
Biomarker Disease Benefit
Monitoring Type 2
Diabetes
Increase drug
effectiveness
Table 2 – Prognostic Biomarker Table 3 – Monitoring Biomarker

6. Clinical decision making is another area of opportunity
where digital biomarkers can play a crucial role,
especially in confirming the presence of a disease.
Autism, or autism spectrum disorder (ASD), refers to a
broad range of conditions characterized by challenges
with social skills, repetitive behaviors, speech and
nonverbal communication. Since, wearables are non-
invasive, discreet, comfortable and convenient, they
are easy for patients to adopt.
By constantly monitoring a patient’s behavior, doctors
can confirm the presence of disease.
Digital biomarkers provide data that can be used to
confirm the presence of autism in patients which was
not possible earlier. It helps in early identification of
rare diseases and increases the possibility to initiate
treatment at an early stage, compared to traditional
methodologies.
Other diseases where diagnostic biomarkers can help
identify the presence of a disease are:
▪ Sweat chloride could be used as a diagnostic
biomarker to confirm cystic fibrosis
▪ Blood sugar or haemoglobin A1c (HbA1c) could
be used as a diagnostic biomarker to identify
patients with Type 2 diabetes mellitus (DM)
5
DIGITAL BIOMARKERS FOR CLINICAL DECISION MAKING
Biomarker Methodology Output
Diagnostic Analysis of facial
expressions, gaze
behavior, and voice
characteristics
Confirming
presence of the
disease
Table 4 - Identifying Autistic Patients
using Diagnostic Biomarker

7. 6
DIGITAL BIOMARKERS: APPLICATION IN DRUG DEVELOPMENT
Incorporation of real-world data in clinical trials are
emerging with the rise of digital biomarkers. There is a
shift in measurement, from snapshot to continuous
measurement of biological processes, pathogenic
processes, and pharmacologic responses.
Consequently, the data collection process has become
faster, more accurate and presents opportunities to
include missed datasets.
In traditional clinical trials, data is collected under
clinical settings and need to follow certain protocols,
which often result in months and years for drug
development. Repeated readings must take place at
different intervals to understand the impact of the
newly developed drug on a particular disease.
Digital biomarkers enable the collection of data in a
home setting, while adding extra data points beyond
the scope of clinical data. This accelerates the data
collection process and reduces turnaround time
necessary for drug development.
In a recent study, patients with neurodegeneration
and cancer condition were stratified into different
groups based on drug responses. It helped in
developing and delivering appropriate medication to
the correct patient.
Biomarker Disease Benefit
Monitoring Type 2
Diabetes
6-Month study allows
collection of readings
outside a clinical setting
Outcome
Incorporate missed opportunities through digital
biomarker to reduce time required for drug
development
Table 5 – Digital Biomarker to Reduce
Data Collection Time

8. 7
DIGITAL BIOMARKERS: TECHNOLOGY STACK
Although digital biomarkers have gained popularity in
clinical decision making and doctors many times use
them to monitor patient health, but regulatory
authorities still need to validate its reliability and
accuracy. Furthermore, integrating the collected data
in a meaningful manner for end users is currently
demanding.
Figure 2 illustrates the flow of data from device to
FHIR server (data curation / integration) to its
utilization via advanced analytics to generate insights.
Digital biomarkers have scientifically defined pillars
that are validated along with its operational
implementation. The ‘scientific consideration’ includes
clinical and analytical validation to complement
hypothesis, while ‘operational consideration’ includes
user acceptability, data logistic, and data collection
parameter.
The resulting data from devices and applications are
stored and mediated through the health system’s FHIR
server (data curation / integration).
With the help of advanced analytics and sophisticated
tools, analysis and insights can be generated for:
▪ Monitoring Patient Health
▪ Dosage Customization
▪ Studying Drug Effectiveness During Clinical Trials
▪ Confirming Presence of Disease
▪ Predicting Health Outcomes
Operational
Implementation
▪ Number
▪ Algorithm Meeting
Minimum Threshold
▪ Continuous
Scientific Consideration
& Validation
▪ Accelerometer
▪ Sensitivity
▪ Detect Unique
Motion Patterns
FHIR Server
Fetching data to generate insights on patient health
Fig. 2 - Digital Biomarkers – Technology Stack

9. 8
▪ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078627/
▪ https://www.nature.com/articles/s41746-019-0090-4
▪ https://ascpt.onlinelibrary.wiley.com/doi/abs/10.1002/cpt.1100
▪ https://www.pharmaceutical-technology.com/comment/digital-biomarkers-healthcare-trends/
▪ https://www.karger.com/Article/FullText/479951
▪ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032822/
▪ https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/
▪ https://www.nature.com/articles/s41746-020-0218-6
▪ https://www.karger.com/Article/FullText/502000
REFERENCES

10. 9
ABOUT THE AUTHORS
Shreejit Nair
Sr. Vice President, Life Sciences
shreejit.nair@citiustech.com
Shreejit has 22+ years of experience across technology consulting, program management, business development,
account management in healthcare, digital convergence and IT outsourcing space with Fortune 100 companies in
US and Europe.​ He leads the Life Sciences market at CitiusTech. He holds a bachelor’s degree in engineering and
master’s degree in business administration.
Sanjivni Sinha
Healthcare BA Consultant, CitiusTech
sanjivni.sinha@citiustech.com
Sanjivni has a strong experience in the area of healthcare in genomics. She has expertise in Good Manufacturing
Practice (GMP) for products, sequencing, and research. She has worked on various healthcare projects related to
sequencing and GMP for leading pharmaceutical and biotechnology companies. She holds a bachelor’s degree in
Biotechnology and Microbiology from NDSU, US and a master’s of science in management degree from Minot State
University, US.

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