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Digital Biomarker
Development
Emily Kunka, MS
WHY DIGITAL
CLINICAL
MEASURES?
The Age of Big Data
We’re in a highly regulated,
trans-disciplinary space and the
line between consumer data and
protected private health data is
being blurred as we use real
world evidence and consumer
devices in clinical studies.
Consumer Health
Data
Real World
Evidence in
Clinical Studies
Bridging The Gap - Case in Point
Willis et al. found that as many as 40% of identified PD patients in
the US never see a neurologist, let alone a Movement Disorder
subspecialist.
Remote monitoring can bridge that gap of identifying undiagnosed
patients, getting them assessed by an expert, and achieve a greater
understanding of their lived experience.
PATIENT
SPECIALIST
Moving Forward With Intentionality
Not only do we need to be more thoughtful, intentional, and
creative in how we measure health, but also in how we
communicate those measures.
We need to turn data into information.
Then, surface the right information to the right people at the
right time.
LANDSCAPE
Back to Basics on (Digital) Biomarkers or “dBM”
● Biomarkers are defined as characteristics that are measured as indicators of
health, disease, a response to an exposure or intervention, including
therapeutic interventions
● Biomarkers can help diagnose a disease or predict future disease severity or
outcomes
● Biomarkers are also used to identify the best treatment for a patient, to
monitor the safety of a therapy, or to find out if a treatment is having the
desired effect on the body
● Digital biomarkers are biomarkers collected using a sensor technology
Framework for dBM Regulatory Acceptance
Needs
Statement
Context of
Use (COU)
Benefit /
Risk
Assessment
Evidentiary
Burden
Required
● Class of dBM
● What is the
question the
dBM is
addressing?
● Improved
sensitivity or
selectivity?
● Consequences
of false
positives or
negatives
● Characterization of
relationship between dBM
and clinical outcome
● Biological rationale for use
of dBM
● Comparison to current
standard
Defining dBM Context of Use (COU)
Unlike COAs which directly measure how someone feels/functions/survives, dBMs can be used
for more than just outcomes in clinical trials and that additional value is defined by dBM type
○ Define recruitment inclusion and exclusion criteria (mainly diagnostic dBMs)
○ Define treatment allocation arms
○ Establish a drug’s proof of concept in a patient population
○ Support therapeutic dose selection
○ Enrich a clinical trial for an event or population of interest
■ Enrich enrollment to a patient subgroup who are more likely to respond to a novel
therapeutic in later Phase II/III trials (mainly predictive dBMs)
○ Evaluate treatment response (pharmacodynamic effect)
○ Provide safety signaling that a patient should stop participating in a clinical trial, or stop
taking therapy if post-market/phase IV trial
○ Lead to adaptation of current or future trial design
○ Support regulatory acceptability of a surrogate endpoint for accelerated approval
End-to-End Process
To move from a concept of interest (input) to an outcome
(output) requires a measurement process
Example in Parkinson’s Disease
1
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Existing Biomarker Classification Construct
● Existing concept with
existing measurement
● Existing concept with new
measurement
● New concept with existing
measurement
● New concept with new
measurement
*Considers COI and outcome
Example:
Novel
Continuous blood
pressure is used to
predict the risk of a
heart attack
Continuous
blood pressure
is used to
predict
depression
Known
Discrete blood
pressure data is used
to predict the risk of a
heart attack
Discrete blood
pressure data is
used to predict
depression
New Biomarker Classification Construct
*Considers COI, measurement process, and outcome
Example in Parkinson’s Disease
V3 Framework
Verification/Valid Clinical Association
Evaluates relationship of sample level sensor outputs to clinical condition of interest
Analytical Validation
Evaluates the performance of an algorithm to convert sensor outputs into
physiological metrics using a defined data capture protocol in a specific population
Clinical Validation
Evaluates whether the physiological metric acceptably identifies, measures, or
predicts a meaningful clinical, biological, physical, functional state or experience in
the stated context of use (COU) and population
“Approved” Biomarkers
“Approved” digital biomarkers are medically validated for
implementation in clinical practice, qualified for a specific
context of use.
“Original” Biomarkers
“Original” characterizes biomarkers from two scenarios:
1. a novel measurement with a known clinical outcome
Ex. gait measurement for Parkinson’s disease
assessment
1. an approved measurement with a novel clinical outcome
Ex. Heart rate to describe an outcome in depression
“Novel” Biomarkers
“Novel” describes a novel measurement such as physical
activity or facial expressions, with a novel clinical outcome
such as depression.
*We don’t know know what we’re going to find or how it
could be used.
Classification Nomenclature
Novel
Novel measurement and
novel clinical outcome.
3
Original
Novel measurement and
known clinical outcome;
approved measurement
and novel clinical
outcome.
2
Approved
Approved measurement
and known clinical
outcome
1
Types of Digital
Biomarkers
Monitoring
Predictive /
Prognostic
Therapeutic
Response
Susceptibility /
Risk
Safety
Diagnostic
Evidentiary Criteria
Universality - to what extent is there evidence across different populations or
drug mechanisms
Plausibility - is the biology of the measure so compelling that it adds to the
weight of evidence for acceptance
Causality - is there a compelling case for it being causal so there is less of a
need of universality
Proportionality - to what extent does the measure explain the disease or the
change in disease
FDA’s Burden of Evidence Guidance
The type and amount of evidence will change depending on
risk and benefit
Broad R&D Use Cases to Consider
● Understand natural history and disease progression over
time
● Better characterize patients
● Digitally collected efficacy endpoints
● Safety monitoring
● Protocol design
● Patient-centered outcomes
Where Are Our Overall Interests in dBM Now?
● Observe new perspectives on the patient that generate new
questions and insights or challenge existing hypotheses
● Better understand patient behavior outside of the clinic
● Show more dynamic changes in the disease over time
● Better understand subgroups of patients / stratification /
profiling / baselining
● Linking behaviors with outcomes
Biomarker Development Process
Discovery &
Development
Q
u
a
l
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fi
c
a
t
i
o
n
/
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v
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Analytical Validation
U
t
i
l
i
z
a
t
i
o
n
Drug Development Tools (DDTs)
● Definition: Methods, materials, or measures that have the potential to
facilitate drug development (biomarkers, clinical outcome assessments, etc.)
● Purpose: To help sponsors optimize drug development and evaluation
● DDTs can be used in an individual drug/biologic application or through
voluntary qualification process outlined in 21st Century Cures Act
● FDA Guidance: DDT Qualification process
● DDTs being considered for qualification are conceptually independent of the
device performing the measurement
● The DDT qualification process is not the same process as the manufacturer
would go through to get the device itself authorized for marketing
VALUE PROPOSITIONS
Value to Patients
Improved
Patient
Insights
Digital Data
Capture and
Processing
Algorithm autonomously
analyzes data.
Actionable Inputs to
Clinical Decision Making
and Care to Improve
Patient Outcomes
To the right people in real
time
Value to Company
Increasing commercial potential by accurate diagnosis earlier
means getting the right therapy to the right patient at the right
time and increased duration of time on therapy
2025
Detecting diagnosis or treatment effect
later without algorithm assistance
2021
Detecting diagnosis or treatment
effect earlier with algorithm
assistance
Linking Surrogate Endpoints to Primary Endpoints
Drug
Activity
Digital
Endpoint
Signal
Achieving
Primary
Endpoint
Greater
Confidence
References
https://www.karger.com/Article/FullText/502000
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5813875/#bibr20-15353702177500
88
https://pubmed.ncbi.nlm.nih.gov/27106660/
https://www.nature.com/articles/s41746-019-0090-4
https://www.fda.gov/drugs/development-approval-process-drugs/drug-developmen
t-tool-ddt-qualification-programs

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Digital biomarker development.pptx

  • 3. The Age of Big Data We’re in a highly regulated, trans-disciplinary space and the line between consumer data and protected private health data is being blurred as we use real world evidence and consumer devices in clinical studies. Consumer Health Data Real World Evidence in Clinical Studies
  • 4. Bridging The Gap - Case in Point Willis et al. found that as many as 40% of identified PD patients in the US never see a neurologist, let alone a Movement Disorder subspecialist. Remote monitoring can bridge that gap of identifying undiagnosed patients, getting them assessed by an expert, and achieve a greater understanding of their lived experience. PATIENT SPECIALIST
  • 5. Moving Forward With Intentionality Not only do we need to be more thoughtful, intentional, and creative in how we measure health, but also in how we communicate those measures. We need to turn data into information. Then, surface the right information to the right people at the right time.
  • 7. Back to Basics on (Digital) Biomarkers or “dBM” ● Biomarkers are defined as characteristics that are measured as indicators of health, disease, a response to an exposure or intervention, including therapeutic interventions ● Biomarkers can help diagnose a disease or predict future disease severity or outcomes ● Biomarkers are also used to identify the best treatment for a patient, to monitor the safety of a therapy, or to find out if a treatment is having the desired effect on the body ● Digital biomarkers are biomarkers collected using a sensor technology
  • 8. Framework for dBM Regulatory Acceptance Needs Statement Context of Use (COU) Benefit / Risk Assessment Evidentiary Burden Required ● Class of dBM ● What is the question the dBM is addressing? ● Improved sensitivity or selectivity? ● Consequences of false positives or negatives ● Characterization of relationship between dBM and clinical outcome ● Biological rationale for use of dBM ● Comparison to current standard
  • 9. Defining dBM Context of Use (COU) Unlike COAs which directly measure how someone feels/functions/survives, dBMs can be used for more than just outcomes in clinical trials and that additional value is defined by dBM type ○ Define recruitment inclusion and exclusion criteria (mainly diagnostic dBMs) ○ Define treatment allocation arms ○ Establish a drug’s proof of concept in a patient population ○ Support therapeutic dose selection ○ Enrich a clinical trial for an event or population of interest ■ Enrich enrollment to a patient subgroup who are more likely to respond to a novel therapeutic in later Phase II/III trials (mainly predictive dBMs) ○ Evaluate treatment response (pharmacodynamic effect) ○ Provide safety signaling that a patient should stop participating in a clinical trial, or stop taking therapy if post-market/phase IV trial ○ Lead to adaptation of current or future trial design ○ Support regulatory acceptability of a surrogate endpoint for accelerated approval
  • 10. End-to-End Process To move from a concept of interest (input) to an outcome (output) requires a measurement process
  • 11. Example in Parkinson’s Disease 1 M e a n i n g f u l A s p e c t o f H e a l t h ( M A H ) I w a n t t o b e a b l e t o w a l k s o I c a n c a r r y m y o w n g r o c e r i e s . 2 C o n c e p t o f I n t e r e s t ( C O I ) A c t i v i t y ( w a l k i n g c a p a c i t y ) 3 C l i n i c a l M e a s u r e m e n t O u t c o m e : W a l k i n g b o u t s p e r d a y E n d p o i n t : P e r c e n t a g e o f p a t i e n t s w i t h > 1 5 % i n c r e a s e i n a c t i v i t y m o n i t o r i n g f r o m b a s e l i n e
  • 12. Existing Biomarker Classification Construct ● Existing concept with existing measurement ● Existing concept with new measurement ● New concept with existing measurement ● New concept with new measurement *Considers COI and outcome Example: Novel Continuous blood pressure is used to predict the risk of a heart attack Continuous blood pressure is used to predict depression Known Discrete blood pressure data is used to predict the risk of a heart attack Discrete blood pressure data is used to predict depression
  • 13. New Biomarker Classification Construct *Considers COI, measurement process, and outcome
  • 15. V3 Framework Verification/Valid Clinical Association Evaluates relationship of sample level sensor outputs to clinical condition of interest Analytical Validation Evaluates the performance of an algorithm to convert sensor outputs into physiological metrics using a defined data capture protocol in a specific population Clinical Validation Evaluates whether the physiological metric acceptably identifies, measures, or predicts a meaningful clinical, biological, physical, functional state or experience in the stated context of use (COU) and population
  • 16. “Approved” Biomarkers “Approved” digital biomarkers are medically validated for implementation in clinical practice, qualified for a specific context of use.
  • 17. “Original” Biomarkers “Original” characterizes biomarkers from two scenarios: 1. a novel measurement with a known clinical outcome Ex. gait measurement for Parkinson’s disease assessment 1. an approved measurement with a novel clinical outcome Ex. Heart rate to describe an outcome in depression
  • 18. “Novel” Biomarkers “Novel” describes a novel measurement such as physical activity or facial expressions, with a novel clinical outcome such as depression. *We don’t know know what we’re going to find or how it could be used.
  • 19. Classification Nomenclature Novel Novel measurement and novel clinical outcome. 3 Original Novel measurement and known clinical outcome; approved measurement and novel clinical outcome. 2 Approved Approved measurement and known clinical outcome 1
  • 20. Types of Digital Biomarkers Monitoring Predictive / Prognostic Therapeutic Response Susceptibility / Risk Safety Diagnostic
  • 21. Evidentiary Criteria Universality - to what extent is there evidence across different populations or drug mechanisms Plausibility - is the biology of the measure so compelling that it adds to the weight of evidence for acceptance Causality - is there a compelling case for it being causal so there is less of a need of universality Proportionality - to what extent does the measure explain the disease or the change in disease
  • 22. FDA’s Burden of Evidence Guidance The type and amount of evidence will change depending on risk and benefit
  • 23. Broad R&D Use Cases to Consider ● Understand natural history and disease progression over time ● Better characterize patients ● Digitally collected efficacy endpoints ● Safety monitoring ● Protocol design ● Patient-centered outcomes
  • 24. Where Are Our Overall Interests in dBM Now? ● Observe new perspectives on the patient that generate new questions and insights or challenge existing hypotheses ● Better understand patient behavior outside of the clinic ● Show more dynamic changes in the disease over time ● Better understand subgroups of patients / stratification / profiling / baselining ● Linking behaviors with outcomes
  • 25. Biomarker Development Process Discovery & Development Q u a l i fi c a t i o n / E v i d e n t i a r y A s s e s s m e n t Analytical Validation U t i l i z a t i o n
  • 26. Drug Development Tools (DDTs) ● Definition: Methods, materials, or measures that have the potential to facilitate drug development (biomarkers, clinical outcome assessments, etc.) ● Purpose: To help sponsors optimize drug development and evaluation ● DDTs can be used in an individual drug/biologic application or through voluntary qualification process outlined in 21st Century Cures Act ● FDA Guidance: DDT Qualification process ● DDTs being considered for qualification are conceptually independent of the device performing the measurement ● The DDT qualification process is not the same process as the manufacturer would go through to get the device itself authorized for marketing
  • 28. Value to Patients Improved Patient Insights Digital Data Capture and Processing Algorithm autonomously analyzes data. Actionable Inputs to Clinical Decision Making and Care to Improve Patient Outcomes To the right people in real time
  • 29. Value to Company Increasing commercial potential by accurate diagnosis earlier means getting the right therapy to the right patient at the right time and increased duration of time on therapy 2025 Detecting diagnosis or treatment effect later without algorithm assistance 2021 Detecting diagnosis or treatment effect earlier with algorithm assistance
  • 30. Linking Surrogate Endpoints to Primary Endpoints Drug Activity Digital Endpoint Signal Achieving Primary Endpoint Greater Confidence