Today’s life sciences and healthcare continuum is fully enhanced by an ever more connected array of sensors and machines. In this setting, pharmaceuticals and medical devices are developed and manufactured in facilities that are fully monitored by sensors. Devices used by clinicians, researchers and patients alike are monitored by an increasing number of sensors. Medication adherence will also be further managed and monitored by sensors. In aggregate, the capture of data from patients provides real-world data of use, misuse and treatment response, on an individual basis.
Data for treatment development
The randomized clinical trial (RCT) has been the gold standard for the efficacy of medical interventions. RCTs, however, face two main obstacles: recruitment and retention. Recruitment requires identifying patients who meet a set of specific criteria, which is difficult and time-consuming.
Between a quarter and two-thirds of trials fail to recruit their required number of participants. Access to larger pools of patient-generated health data (PGHD) — much of it derived from connected sensors and health and wellness apps — enables the identification and recruitment of suitable study candidates.
Once patients participate, data from wearables and other monitors provide closer contact with the study team, while reducing the need for the subject to travel to a research center for periodic evaluation, which improves retention.
Such monitoring also provides more precise information about adherence to the study protocol. Efficacy numbers can then reflect the actual use of a medication, as well as reveal the full impact of side effects or other obstacles to proper use, providing novel insights to effectiveness. Engaging patients in managing their own care in trials may also streamline future clinical development workflows.
Real-world data (RWD), gathered from regular monitoring of a wide range of patients over long periods of time, provides detailed information about how treatments work in daily life for more patient types than could be included in an RCT and, once analyzed, becomes real-world evidence (RWE) that enhances therapeutic effectiveness. RWE is also used to monitor post-market safety and adverse events for both biopharmaceuticals and medical devices, improving care decisions and therapeutic guidelines.
Precision medicine emerges
RCTs can fail to provide meaningful insight when some part of the population responds strongly to a treatment while others don’t. RWE can help stratify patients into subpopulations that are more susceptible to a particular disease or have a specific treatment response. Additional data, such as genomics and other biomarkers, may help identify the mechanisms behind these responses. Combined, these expanding sets of clinical and ambient data allow for more specific targeting of patients for the appropriate trials.
Many diseases are increasingly seen as a complex of several diseases with similar symptoms, which can be divided up and defined by mechanistic pathway (endotype), specific clinical presentation (phenotype), and individual patient susceptibility (genotype).
This growing knowledge of diseases can enable a more precise balancing of interventional benefits and risks. For example, Herceptin is a breast cancer drug that is an effective treatment for women with too many Her2 protein receptors in their tumors, which are caused by an overexpression of the Her2/neu gene. Herceptin works less well for tumors without excess Her2 receptors, but for all patients it increases the risk of heart dysfunction. So, a test for the Her2/neu gene enables targeting this treatment for maximum benefit while minimizing exposure of some patients to unnecessary risk.
A more granular understanding of disease states leads to better-defined RCTs and more narrowly defined and targeted treatments. Such precise therapies will increasingly challenge manufacturing processes as they demand low-volume production runs of specialized therapies aimed at specific populations at the same time as overall capacity is kept high.
Even narrower are personalized therapies that use the patient’s own cells as the basis for treatment. An example is autologous CAR-T cancer therapy, which extracts T cells from a patient’s blood, genetically reengineers them to recognize and kill cancer cells, and then infuses those cells back into the patient. Currently, these processes can’t be carried out in the same location and require a complex supply chain with strict temperature and custody regulation and tight timelines.
Looking forward, facilities may grow smaller and more localized, promising more localized manufacturing, shorter supply chains and quicker response.
