Time for Digital Selection Biomarkers

By Arthur Combs, Senior Clinical Advisor

Did you know that the use of selection biomarkers in clinical development improves the success rate in every phase (I-III)? And that use of selection biomarkers improves the overall success from Phase I through regulatory approval more than 3-fold? Examples include using tumour markers to select patients for cancer therapy trials or knowing the ejection fraction (EF) for heart failure patients since those with preserved EF behave and respond differently from those with reduced EF.

Unfortunately, many patient selection processes are dependent on an overarching inclusive diagnosis, a subjective rating scale or both. These are opportunities for the use of actigraphy to objectively and precisely characterize the functional status of study candidates. Oncology, neurodegenerative diseases, and many others depend on subjective rating scales for patient selection. For example, oncology subjects are often selected based on an the Eastern Cooperative Oncology Score (ECOG) score.

This score characterizes the ability of a subject to perform daily activities and any restrictions in place. Clinical development studies often select ECOG subjects judged to be ECOG 0, 1 or 2 to ensure that a subject is able to tolerate the study therapy. But performance and functional data from wearable technologies show that what were thought to be homogeneous [ECOG] groups with a given score, in actuality are groups that demonstrate significantly heterogeneity in their total activity, step counts, stairs climbed and other actigraphy metrics.

It is time to consider true functional status as a digital biomarker of patients’ suitability for a given trial. Given the benefit of using selection biomarkers in clinical development, the development and application of digital selection biomarkers through use of wearable sensors and functional assessment in real-world settings could significantly improve both the efficiency and success of clinical development trials. This is especially true in conditions where the selection of study subjects is based on broad diagnosis, subjective classification, surveys and patient-reported outcomes.

Digital Measures - Is it Validated?
Part Two

If you missed it, you can read part one here.

 

By Stephanie Sargeant, Commercial Manager

We know the use and impact of digital measures, biomarkers and endpoints continues to explode. The terminology itself can be confusing and difficult to navigate. Depending on an individual’s involvement in the trial, stakeholders may still not always be making the correlation when selecting wearables within trials, for the importance of thinking the trial data through to the end.

Selection teams may get deterred with wanting to include every sensor type because it’s available at the cost of long-term wear and validated data - topics we continue to educate on within clinical trials. Yes, adherence and compliance are vital, but when we cut to the chase, the technology selected is a means to an end – it’s all about the data! In selecting the appropriate technology it’s therefore key to consider:

• What is the objective of the trial?
• What are you trying to achieve?
• What are the most insightful measures available for both patient and clinician?
• Then, the big one - are they validated?

Asking about validation can be a loaded question, the industry needs to be clear about what it means and is trying to achieve here - we often see a disparity in the expectations of study design teams with the readiness of off-the-shelf, validated algorithms. The sector is evolving rapidly in terms of the implementation of digital measures within clinical trials and open-source algorithm development, along with the approach from regulators such as the MHRA and FDA, who have recently released guidance on Digital Health Technologies for Remote Data Acquisition in Clinical Investigations. When combined with the nuances of different population groups, therapeutic areas and digital measures are considered – there’s still a long journey to go on.

In the meantime, Activinsights remains committed to leading the way in objective digital health measurement. Keeping it simple and accessible with a clear pathway to validation, alongside ongoing algorithm development for relevant digital measures across therapeutic areas. We’ve identified over 150 digital measures we regularly use in data analysis – watch this space for more information coming soon here!

There is a clear route being formed on more readily available, validated digital measures using ‘off-the-shelf algorithms’ alongside the need for ongoing algorithm development to identify novel measures, specific to population groups. This can often be done in parallel to a clinical trial. As digital health measurement experts, it’s our job to support study trial design, scientific and digital biomarker teams with trials co-coordinators to navigate this space. Combining easy deployment, relevant, meaningful and validated digital measures to improve study efficiency and novel insights outside the clinic with remote patient monitoring.

Be patient – as an industry, it’s on its way!

 

 

Digital Measures - Is it Validated?
Part One

By Stephanie Sargent, Commercial Manager

It’s often the big question when discussing the data from wearables, and so it should be – ‘is it validated?’

This is vital for several reasons. Study teams need to consider regulators, patient population groups, therapeutic areas and traceability on generating the most accurate and reliable data possible within a clinical trial. So, what is ‘validation’?

The Digital Medicine Society (DiME) provides a useful definition and tool, known as the V3 framework – which encompasses verification, analytical validation, and clinical validation (V3). The three-component V3 framework combines established practices from both software and clinical development to establish the shared foundation for evaluating whether digital clinical measures are fit-for-purpose.

At Activinsights, we are proud of over a decade’s heritage in public health research across 200+ global institutions, reaching 40+ countries. Our focus in the last couple of years has been to bring learnings across from the research sector into the clinical trials landscape for relevant and meaningful digital measures for both the patient and clinician – where there are now 100 + clinical trials operating with Activinsights’ technologies across 20+ therapeutic areas.

A breadth of knowledge around validation in different population groups and digital measures can be translated into digital clinical measures from peer-reviewed public health research. An example includes Activinsights' supporting metrics in the objective monitoring of fidgeting behaviours, where early work has been done in children with Autism Spectrum Disorder. This knowledge in algorithm development may also be relevant for dementia trials, as an increase in night-time wandering behaviours has been identified as a biomarker for disease progression. This is where continuous remote monitoring comes into its own for valuable insights on objective lifestyle and disease progression alongside drug efficacy, outside of clinic visits.

Throughout the trial, it remains key that collecting objective data is unobtrusive and of low participant burden. Another example of the benefit of algorithm development from peer-reviewed, open-source raw data formats is understanding steps in more detail for specific therapeutic areas. Steps appears to be a commonly requested measure within clinical trials, likely because it’s easily interpretable across both clinical and patient population groups.

So, the client says: ‘Do you have a step measure available and is validated?’

Activinsights' reply: ‘Yes…’

However, it’s not a one-size fits all approach. We have validated steps measures; but ultimately it depends on the population group and therapeutic area we’re talking about. Not all steps are measured the same. A healthy child may take the same number of total steps as a Parkinson’s patient, but how often and when, along with the gait information around those steps could be dramatically different. It demonstrates how inextricably linked algorithm development and validation are, and it is not as simple as selecting an ‘off-the-shelf algorithm’ that is validated for a one-size-fits-all approach within clinical trials.

DiME have recently highlighted the challenge that different measures presents across the clinical trials industry, and the need for a core set of digital clinical measures that are accepted with evidence as a starting point. It highlights the importance of removing vendor biases with black-box algorithms, hence the trend of working in an open-source environment with reproducible raw data sets, like the GENEActiv, to drive the sector forward.

Benefits of the objective measurement of physical activity

By Amy Bates, Marketing Executive

Insufficient levels of physical activity are associated with 1 in 6 deaths in the UK and are estimated to cost the UK £7.4 billion annually (including £0.9 billion to the NHS alone). As a result, lack of physical activity is one of the top ten causes of death globally and is a predominant risk factor for non-communicable diseases (NCDs) such as cardiovascular disease, cancer and diabetes. Such statistics have led the World Health Organisation to declare physical inactivity a global public health problem.

Changes in lifestyle and increasing urbanisation are partly responsible for the insufficient levels of physical inactivity within populations. To tackle this, the NHS is pushing to develop new preventative healthcare strategies, many of which involve identifying behaviours that increase the risk of poor physical health.

Most people are unable to assess their levels of physical activity accurately and reliably. Generally, estimation of perceived activity will be a great deal higher than the reality. Therefore, the use of objective tools, such as accelerometers, to measure activity levels are rapidly superseding the use of self-reported questionnaires.

Accelerometers are now widely used within the research community to conduct population-level studies to assess health and performance. These unobtrusive, wrist-worn devices allow accurate data to be collected, revealing a more complete picture of physical activity and sleep patterns in the participant’s normal environment, without the risk of self-report bias.

The collection of raw data in a real-world environment by researchers is instrumental in understanding the intricacies of people’s daily lives. This unfiltered data resource holds such depth of information but is still very much underused.

By increasing the use of scientifically validated technologies (such as the GENEActiv) to measure activity, healthcare professionals can see greater insights into patient behaviours and ultimately use this to inform preventative healthcare strategies.