Value of digital biomarkers in precision medicine: implications in cancer, autoimmune diseases, and COVID-19

Based on the patient’s characteristics, precision medicine (PM) aims to optimize the time of administration of the most appropriate medicine with the minimum risk of toxicity. This is a multidimensional problem due to the varied disease course and therapeutic responses of patients. General factors, such as genetics, epigenetics, environment, ethnicity, adherence, lifestyle, and diet, determine these outcomes. In clinical trials, some drugs may be not beneficial or even harmful for a given ethnic or co-morbid group. Partial response is also observed outside trials, as the most commonly used drugs show high efficacy in relatively few patients. Therefore, what we call ‘imprecise medicine’ is the first challenge of PM due to the assumption underlying clinical practice that disease treatment and prevention strategies developed at the population level are expected to be accurate when applied at the individual level. The complexity that drives the variations in patient profiles depends on the heterogeneity of information obtained from large volumes of genetic, serological, biochemical, and diagnostic imaging data. These represent dimensions that need harmonization and integration with lifestyle and environmental factors. The second challenge is with assessing the benefits of the data dimensions, such as diagnostic improvements, earlier interventions, increased drug efficiency, and better-targeted treatments. To accommodate the heterogeneity of the etiologies, clinical symptoms, and treatment responses of patients in clinical practice, a revised clinical approach is recommended [1]. The first step is the development of a machine learning (ML)-assisted risk assessment model (see, for instance [2],) followed by the identification of the robust multimodal data-driven prognostic indicators (see, for instance [3],). These two efforts require new strategies for integrating heterogeneous information from different structured and unstructured data sources (electronic health records (EHRs), administrative databases, bioimaging archives, self-quantified measurements, etc.). Big Data has introduced a new paradigm for population-based studies that comes with challenges. For instance, the validity of such studies is based on the diagnostic accuracy used for all cases. A critical problem is the variability of the methods used to perform validations. Currently, there are challenges with validating most disease classification algorithms, and this complicates the assessment of their potential for population studies. Model validation facilitates safer interpretability of the correlations between diverse data types revealed by the models. Data-centric perspectives of complex diseases facilitate their definition as heterogeneous processes that have multifaceted causes, courses of evolution, treatments, and patient’s disease trajectories from the observed responses to treatment (see [4–6], among many other examples). These trajectories differ with each patient and, therefore, necessitate a precision approach. We emphasize the necessity of early intervention when molecular causes/patterns can still be identified. Early treatment is likely to lead to a substantial reduction in the risk of disease progression and prolonged health. Thus, it is critical to develop more inclusive digital biomarkers (DBs) [7,8] that may reflect the synergism of clinical and molecular data for identifying diseases at the early stages when interventions have optimal chances of success and future damage prevention. The DB values should be proportional to the ability to shorten the length of the trajectories during the disease course, which will reduce the temporal window of opportunity between any disease trigger and a clinical intervention before irreversible damage occurs.