Digital Biomarkers in Neurology

Digital biomarkers are a set of characteristics and properties collected from digital health technology, which is measured as an indicator of the biological, pathological process and the response to any exposure or intervention which can be therapeutic

Introduction

  • Digital biomarkers (DBMs) are objective quantifiable data points (endpoints).
  • They are collected with the help of digital devices which are
    • Wearables
    • Digestible capsules
    • Implants
    • Wearable tools such as watches to track steps for fitness.
    • Digestible capsules are used to record data for gas volume
    • Implantable tools to measure heart rhythm
  • DBMs are a crucial part of digital health technologies.

Defining Digital Biomarkers (DBMs)

💡
According to the Food and Drug Administration (FDA), “Digital biomarkers are a set of characteristics and properties collected from digital health technology, which is measured as an indicator of the biological, pathological process and the response to any exposure or intervention which can be therapeutic.”

Defining Digital Health Technologies

💡
Definition of digital health technologies (DHT)
A system that uses computing platforms, connectivity, software, and sensors for healthcare and related uses.
Examples:
1.Wellness apps that reminds a person if stays long in sun to limit UV exposure.
2.Apps to send reminder for medicine reminders, so it helps in adherence to treatment.
3.Sensors that can collect data and latter can help to study and develop medical product.
 

Figure 1: HealthIoT tools, measures, and devices

notion image
 

Table 1: Types of data collected by wearable devices

Types
Definition
Examples
Passive Data
Passive digital biomarker data is collected from sensors that are integrated when the user wears the devices
Accelerometer, Gyroscope, Location, and distance traveled.
Active Data
Active digital biomarker data is when the user interacts with the devices or gives any input.
Cameras, Forms, Voice, Symptom reporting

Figure 2: Passive-Hybrid-Active Continuum of data collection example of sleep monitoring

notion image
Source: Coravos, et al. (2019). Digital Medicine: A Primer on Measurement. Digital Biomarkers3(2), 31–71. https://doi.org/10.1159/000500413

Understanding Digital Endpoints

Defined as “ Data generated by sensors outside the clinical environment, for example in a patient's free-living conditions.”
  • There are two types of digital endpoints
    • Digital Biomarkers (DBMs)
    • Electronic Clinical Outcome Assessment (eCOA)

Table 2: Type of Digital Endpoints

Type of Endpoint
Description
Modality
Digital Biomarkers
Indicator of a biological and pathological process
Objective
Clinical Outcome Assessment
Direct measure of how the patient feels, survive, and function.
Subjective

Table 3. Categories of Digital Biomarkers

Biomarker category
BEST definition
Digital biomarker example
Diagnostic biomarker
A biomarker used to detect or confirm the presence of a disease or condition of interest or to identify individuals with a subtype of the disease
An algorithmic classification of cardiovascular features extracted from optical sensors on wearable devices to identify atrial fibrillation
Monitoring biomarker
A biomarker measured repeatedly for assessing the status of a disease or medical condition or for evidence of exposure to (or effect of) a medical product or an environmental agent
An accelerometer-based sensor device that collects data about chest and limb movement to measure gait in patients with Huntington’s Disease
Pharmaco-dynamic/Response biomarker
A biomarker used to show that a biological response has occurred in an individual who has been exposed to a medical product or an environmental agent
A wrist-worn DHT may collect accelerometer data and use the data to detect physiological changes (e.g., tremor and bradykinesia) in response to a pharmacological agent

Electronic Clinical Outcome Assessment (eCOA) types

  • Electronic Clinical Outcome Assessment (eCOA) is a digital record of subjective assessment in different domains of the patient journey described in Table 4.
    • Electronic Patient-Reported Outcome (ePRO)
    • Electronic Performance Outcome (ePerfO)
    • Electronic Clinician-Reported Outcome (eClinRO)
    • Electronic Observer-Reported Outcome (eObsRO)
 

Table 4: Types of Clinical Outcome Assessment (eCOA)

eCOA
Definition
Example
ePRO
Information reported by patients with the help of electronic devices
Rating of pain activity or severity of migraine asking them to grade on 1-10 scale
ePerfO
Measuring the functional ability of participants using electronic devices
Tapping exercises on a smartphone for bradykinesia in parkinsonism/memory recall in dementia.
eClinRO
Clinician observing the participants and grading them
Alzheimer’s disease who is unable to report so clinician will report.
eObsRO
Life partner, caretaker, parent observing the patient
Unconscious patient observed by any person

Application of Digital Biomarkers

  • Digital biomarkers are easy to access, cheaper , providing continuous health data not restricted to just visits.
  • Creates more precise, personalized plan getting detail at cellular level helping in field of precision medicine.
  • Digital biomarkers can help in managing patient according to severity of disease.
💡
Recently, in March 2022 FDA (food and drug administration) issued graft guidance providing its recommendation on the use of digital health data in clinical investigations.
The digital biomarkers (endpoints) are used by the digital medicine society in partnership with multiple pharmaceutical companies, for the launch of 3Ps of digital endpoints(3Ps), which is a tool kit of resources created to facilitate the inclusion of digital endpoints as evidence for payers and helping them in reimbursement and development of the new medical product.

Table 5: 3Ps of Digital endpoints in Tool kit Resources

Tool kit Resources
Description
Project/ Patient toolkit
Patient groups that are vital to establishing the value of evidence derived from digital endpoints
Pharma toolkit
Companies developing new drugs and medical products are evaluated using data derived from digital endpoints
Payer toolkit
Organizations making reimbursement decisions about new drugs explain their efficacy using data derived from digital endpoints

Digital biomarkers in clinical care

  • Clinical care can be done remotely.
  • The care team should be able to provide the summary with red flags so, that patient should know when to report.
  • For quality digital care, these two things should be kept in mind:
    • Clinical practice guidelines.
    • Care pathways.
  • The care team should be able to optimize the genetic data with the storage data.
    • For example, Abilify MyCite is a product consist of aripiprazole tablets in software-based ingestible event marker for tracking drug intake.
 

Table 6: Real-Time Use of Digital Biomarkers

Disease/Conditions
Tool
Digital Biomarker
Measures
Data from patient
Social Anxiety
Software app
1. Heart Rate 2. Heart Rate Variability 3. Exposure to light social contact 4. Location-based marker
1. Level of anxiety 2. Triggers of anxiety 3. Risks of Anxiety
1. Passive 2. Active How does the patient text or talk on the phone
Parkinsonism
Smartphone app
1. Tapping 2. Speed of typing
1.Vocal assessment 2.Motor coordination
1.What is the quality of voice 2.Tone of the voice 3.Movement of the hand while holding a phone
Tremors
Touchscreen sensors, wearable wrist device
1.Hand movement (amplitude and length in resting and during movement).
Fine motor skills
1.Can detect human tapping 2.Swiping 3.Typing
Ataxia
Inertial sensors (accelerometer and gyrometer)
1. Step variability 2. Lateral sway
Gait metrics
1 .Can detect human motion 2. Posture
Rehabilitations (orthopaedic surgery)
App-enabled wearable sensors
Posture cubic
Monitoring rehab exercises
1. Range of motions 2. step counts
Stroke, tumor
Camera, wearable devices
1.Mapping metabolic equivalents(MET) to energy level. 2.Stride count 3.stride duration 4.normalized stride count 5.posture cubic
1. Eye co- ordination and reaction 2. Physical activity, 3. Functional range of movement analysis
1. Detect eye movement 2. Pupil dilation and expressions
Alzheimer’s disease
Smart devices
Digitized cognitive testing for focus, verbal memory and attention.
Cognitive impairment
A survey of 10-minute set of activities that are designed for cognitive impairment while doing different aspects of day-to-day activities
ADHD (For patients in trial of stimulants)
Wearable cardiac monitor
Irregular heart rate
Arrythmia
Irregularities in heart rate
Multiple Sclerosis
Smart phone app
1.Typing speed 2.How long the key is pressed 3.Latency of key 4.Errors in Punctuation marks 5.Words space error
1. Motor defect 2. Cognitive Defect
1. Clinical Arm functionality 2. Disease severity
 

Figure 3: Developing measures that matter, a framework for creating digital biomarkers

notion image
Source: Developing and Selecting Digital Clinical Measures That Matter to Patients – Digital Medicine Society (DiMe), 2021)

Digital biomarkers in clinical research

Clinical trial lifecycle

💡
Design and feasibility studies lead to the launch of a study, followed by a trial, and eventually the development of a regulatory submission.
  • The researchers design the Digital Clinical Trial protocol in advance and discuss it with the sponsoring firms.
  • This protocol is disseminated digitally to the individuals who voluntarily consented to participate in the study.
  • These participants are then randomly exposed to the specified medical intervention.
  • Participants process their own data consisting of a number of digital biomarkers.
  • Findings are then gathered, organized, analyzed, and sent back to the study team.

Novel endpoints

  • Novel endpoints are used for clinical trials.
  • There are multiple positive effects of developing novel endpoints in clinical trials, which are short, medium, and long term, concerning patient care, efficacy, and efficiency.
  • Evidence-based, and stakeholders do collaborative efforts to get better results.
  • Novel endpoints are gaining importance as they add the complementary needs of the studies.
  • Used to identify the gaps and barriers.
Advantages Of Novel End Points In Interventional Trials
  • Collects data of patients outside the clinic including the daily chores.
  • Create alliance amongst the healthcare ecosystem i-e payers, sponsors, and patients.
  • Some promising treatment is rejected before phase 3, novel endpoints help to go it to phase 3.

Foundation Of Determining Fit For Purpose Biometric Monitoring Technologies(BioMeTs)

  • The evaluation of BioMeTs, can fit to purpose can be use in clinical trials.
  • Design Specification and Modular Prototyping
  • A three- component framework is proposed for foundational evaluation of BioMets, which includes
    • Verification
      • BioMets, is the technology which evaluates and demonstrate the performance of sensor, by comparing the sample level data with pre-specified set of criteria.
    • Analytical Validation
      • Evaluates the performance of algorithm, and the ability of BioMeTs to measure, detect, predict, behavioral and physiological metrics.
    • Clinical Validation
      • Evaluates if BioMeTs identifies, measures and predict meaningful clinical, physical experience in a stated context of use which includes specified population.
  • Clinical Utility

Neurological ongoing trials using digital biomarkers

  • Phase 2 study (2016)
    • To measure the seizure activity as assessed by Change in Electrodermal Activity (EDA) from Visit 1 to Visit 9 (Up to 8.5 months) in Rett Syndrome.
  • Phase 0 study (2017)
    • To assess variability in Cognitive Performance using the prime outcome measure; high-frequency mood capture in Major Depressive Disorder (MDD)
  • Phase 1b study (2018)
    • To measure gait task with smart phone in trouser/belt walking (20yards) turning around and returning to starting point (degree/sec) to determine any gait abnormality at earliest stage.
  • Phase 0 study (2018)
    • Detection of brain changes related to mild cognitive impairment and dementia due to Alzheimer’s disease using CG scores(for accuracy and reaction speed).
  • Phase 4 study (2020)
    • Determine the changes in Parkinson’s Disease symptoms as assessed by Parkinson’s Kinetigraph/Personal Kinetigraph(PKG) by wearable devices(baseline to week12).
  • Phase 2 study (2021)
    • Employing a 3D optical motion capture system (recording upto 10 weeks) to determine the effect of deutetrabenazine on facial movements at rest and during functional task performance in Huntington’s Disease.
  • Phase 4 study (2021)
    • Define a correlation between digital outcome assessments (DOAs) and MRI measures of brain tissue damage in people living with Multiple sclerosis(MS).

Digital Biomarkers In Public Health

  • Digital biomarkers are used in clinical trials, which can be cross sectional and longitudinal.
  • Providing us
    • Easy and remote access
    • An unbiased, reliable , patient centric data
    • Create an infrastructure for predictive diagnosis.
  • Digital biomarkers in predictive diagnosis
    • Predict the effectiveness of medical treatment by tracing patient recovery,
    • Can help detect disease and
    • It can also be very transformative in near future.
  • It can transform healthcare, by early diagnosis of diseases like Alzheimer’s helping us to get the preventive approach and delaying the onset of disease with lifestyle changes.
    • CognICA integrated cognitive assessment (ICA) test is developed by London based company to test display images at rapid pace on iPad screen and asks users to identify them as animals or non animals.
    • Recently, FDA has approved an artificial intelligence based test for early detection of dementia on Ipad in five minutes.
    • The ability of health brain to process images of animals is in less than 200 milli seconds.
    • This test helps us detect early, before severity of the disease begins.
  • In multiple sclerosis, vision is mostly affected, presented mostly in the form of optic neuritis, measurement of the retinal nerve fiber layer can be used as digital biomarker,
    • Regular, monitoring of peripapillary retinal fiber layer, can be used as to monitor the disability in multiple sclerosis.
  • With the use of digital biomarkers, diabetes mellitus type || may be prevented in targeted population, by analyzing the risk factors.
    • Encouraging to wear fit bit watches.
    • Like promoting more walk in obese patients.
  • If we talk about the future applications in digital biomarkers, the creation of digital biomarker panels, which has both traditional and digital biomarkers giving it better way to explain human health and disease.
  • In Precision medicine, digital biomarker can play a major role.
    • As it can be used to facilitate new drug development.
    • To develop targeted therapies.

Limitations

  • Lack of regulatory oversight
  • Limited funding opportunities
  • General mistrust of sharing personal data
  • Shortage of open-source data and code
  • Standards and validation methods in digital biomarker research is limited

Conclusion

💡
The digital biomarkers should undergo significant research like traditional biomarkers to ensure they are accurate, and reliable demonstrating specificity and sensitivity, noninferiority, equivalence, or superiority.
  • With the wealth of new data, researchers, clinicians, entrepreneurs and consumers better understand states of both health and disease by the help of digital biomarkers.
  • Digital biomarkers are consumers generated physiological and behavioral measures collected through digital tools creating digital data, that can be be used, influence and predict health related outcomes especially in the field of neurology and psychiatry.
  • The future of medicine, lies with the advancement of artificial intelligence and implementation of digital biomarkers in figuring deadly disease with smart devices and digital biomarkers.
  • We expect some devastating revolution in nearly all the fields of medicine with the usage of digital biomarkers.

Bibliography

  • Neurodiem. (2022). Neurodiem, your daily connection to neuroscience. Neurodiem. https://www.neurodiem.com
  • The Future of Digital Biomarkers. (2021). Gehealthcare.com. https://www.gehealthcare.com/article/the-future-of-digital-biomarkers
  • Taylor, P. (2022, May 26). Cumulus puts its digital biomarkers for CNS diseases to the test. Pharmaphorum.com; Pharmaphorum. https://pharmaphorum.com/news/cumulus-puts-its-digital-biomarkers-for-cns-diseases-to-the-test/
  • healios. (2021, September 19). Healios Digital Health Platform - Empowering Neurology and Clinical Trials teams through novel digital biomarkers and remote monitoring. Healios.io. https://healios.io/dreams/
  • Ontario Brain Institute. (2020). Webinar - Digital Biomarkers in Brain Health [YouTube Video]. In YouTube. https://www.youtube.com/watch?v=WHTOmh9--Uo
  • Mahadevan, N., Demanuele, C., Zhang, H., Volfson, D., Ho, B., Erb, M. K., & Patel, S. (2020). Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. from https://www.nature.com/articles/s41746-019-0217-7
  • Coravos, A., Goldsack, Jennifer C., Karlin, Daniel R., Nebeker, C., Perakslis, E., Zimmerman, N., & Erb, M.  Kelley. (2019). Digital Medicine: A Primer on Measurement.
  • Jacobson, N. C., & Bhattacharya, S. (2022). Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy149, 104013. https://doi.org/10.1016/j.brat.2021.104013
  • Roadmap to patient-focused outcome measurement - Google Search. (2013). Google.com. https://www.google.com/search?q=Roadmap+to+patient-focused+outcome+measurement&source=lnms&tbm=isch&sa=X&ved=2ahUKEwii97vgzqL4AhUepokEHdd2CZcQ_AUoAnoECAEQBA&biw=1536&bih=848&dpr=1#imgrc=z63D1w2vD0APlM