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Multiple biomarkers are used to empower more accurate patient stratification. As depicted in Fig.
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By evaluating dynamic data on tissue-based parameters, e. As illustrated in Fig. Each tumor phenotype is associated with specific underlying biological and pathological mechanisms that may determine the success of the host immune response and immunotherapy or other therapeutic modalities to fight cancer.
Identifying these mechanisms at the level of the individual groups of patients and selecting those patients with similar tumor phenotype is critical for the selection of specific patient populations both for the development as well as implementation of therapeutic interventions. Schematic of an integrated biologic information for a targeted therapeutic intervention. Increasingly, many smartphone apps are also available for health management with or without connection to these sensor devices [ 43 , 44 ]. There are approx. Recently, a new class of wearable smartphone-coupled devices such as smart watches have been widely available.
These devices offer new, and more practical opportunities not without limitations [ 44 ]. As those wearable devices and their corresponding apps continue to develop and evolve, there will be a need for a more dedicated research and digital expert assessment to evaluate different healthcare applications as well as assess the limitations and the risks of impinging on the individual privacy and data safety. However, the problem is that a majority of these apps and devices are meant for wellness purposes and not intended to diagnose or treat diseases.
As reported previously in the literature [ 5 ], and shown Figs. The evolving field of machine learning and artificial intelligence with the support of human interpretation will have a dramatic impact on the field [ 45 , 46 ]. This field has already generated tangible results.
Indeed,, medical device companies e. Pharmaceutical companies are also moving in the same direction. Thus, developing digital biomarkers, big data analysis and interpretation will be beneficial in the new era of PM. In a typical clinical trial or in a clinical setting, the patient visits the hospital not more than once per month or less. So, the clinician can observe the signs and symptoms of the patient only during this visit and has almost no visibility on how the patient is doing for the majority of the time outside the clinic.
This allows the collection of quantitative and unbiased data on a frequent or almost continuous basis. The clinician can get almost real-time feedback on each patient, whether they are getting better or worse. They also outline the benefits of using digital BMs in clinical trials such as being patient-centric while also making faster decisions that save time and costs. The first and most important consideration in developing digital BMs is not which device to use, but rather deciding which disease symptoms to capture that best represent the disease.
Involving patients, and physicians in the discussion are necessary to understand which symptoms matter to patients. At the same time, it is important to consider if these symptoms can be objectively measured and what is a meaningful change in measurement that reflects treatment benefit.
Once it is clear what endpoints need to be captured, the right device can be selected. The device technology needs to be verified measurement errors, variances, etc. An observational study is required to ensure the suitability of the device before deploying it in a trial. Heart disease and diabetes measurements are common application areas for sensor-based devices. However, digital BMs could have the most impact in monitoring CNS diseases since it gives us the opportunity to measure symptoms that were largely intractable until now. Various sensor devices are available for tracking several aspects of health such as activity, heart rate, blood glucose and even sleep, breath, voice, and temperature.
Most smartphones are equipped with several sensors that can perform the various motion, sound and light based tests. In addition, the smartphone can be used for psychological tests or to detect finger motions through the touchscreen.
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These measures can be used in various combinations to predict the health aspects or symptoms required. Digital BMs can have several applications beyond clinical trials, for example in diagnostics—to identify patients affected by a disease.
Digital BMs present a big opportunity for measuring endpoints in a remote, objective and unbiased manner that was largely difficult until now. However, there are still several challenges that need to be considered before developing and deploying them to measure endpoints in clinical trials. There is a wrong notion that by the time a BM is discovered and validated; it is too late to affect the decision-making process.
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The real question is whether the chosen BM is: 1 intrinsically related to the pathogenesis of a disease; and 2 whether it is reliable and adequate for decision-making. It has been reported that building computer models can transform potential BM into clinically meaningful tests. However, on several occasions when scientists [ 47 ] attempted to import data from the literature, they found that the diagnostic criteria used to assess BMs accuracy were vague or based on un-validated BMs.
Identifying BMs that can be translated from animal models to humans is also challenging [ 48 ]. While inhibiting an enzyme in an animal model may be effective, this may not be the case in humans. This is either because the pathway has diverged or humans have some compensatory mechanisms. A treatment may change a BM, but this may be irrelevant to a specific disease. Therefore, a true BM must be intrinsically linked to the pathogenesis of the disease. A drug should treat a disease, not a BM. Without understanding the pathogenesis of a disease, it is difficult to figure out what is the right BM to be used in clinical studies.
Once a BM is identified, it is difficult to understand whether it is associated with a specific disease or multiple diseases or if it is a reflection of poor health.
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A growing body of evidence indicates that SLE is associated with increased risk of cognitive impairment and dementia [ 49 ]. Otherwise, the plethora of BMs that has been generated would be irrelevant. Pharmaceutical companies are obsessed with the idea that a BM needs to be validated before it can be used for decision-making. Unfortunately, there are no clear-cut criteria to date identifying which BM should be validated.
The rigor on how to use a BM to kill a compound relies entirely on the discretion of pharmaceutical companies. The risk of using the wrong BM or selecting the wrong set of BMs may lead to the wrong decision of dumping a good drug because the adopted BM strategy was evaluated inaccurately.
To overcome this problem, pharmaceutical companies tend to rely their decision-making process on a long list of BMs very often too many. This is based on the notion that clusters of variables can be used to differentiate responders from non-responders. The risk of utilizing a long list of BMs is not only costly but also to make the data difficult to be interpreted. The best solution to this problem is to select a strategy that selects a few BMs with complementary predictive properties. In the last few years, the FDA has pressured pharmaceuticals to shift the paradigm towards PM, thus targeting diagnostics and treatments based on patient-stratification.