The Digital Transformation of Clinical Trials: What you need to know

The Digital Transformation of Clinical Trials: What you need to know
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by Sanjeev Kapoor 29 Jun 2020

Clinical research is one of the most vital activities that help prolong human life expectancy while improving Quality of Life for patients. It comprises research activities that validate hypotheses about biomedical or behavioral interventions associated with the treatment and management of diseases. These activities include experiments and observations involving human participants, which are commonly known as clinical trials. Clinical trials are a prerequisite activity towards validating the safety and efficacy of new drugs, prior to introducing them to the market. The costs of clinical trials can be several billions of dollars, which are spent not only in cases of approved drugs but also in cases of failing trials. This reinforces the socio-economic importance of clinical trials, as they play an important role not only on the quality of healthcare but also in the budgets of governments, pharmaceutical companies, and biotechnology organizations that are commonly sponsoring clinical research activities and bear the costs of clinical trials. Therefore, most governments and healthcare organizations worldwide are seeking for ways to increase the efficacy and accuracy of clinical trials results, while at the same time optimizing their costs. The digital transformation of clinical trial activities could offer significant benefits in this direction.

 

Digitizing Clinical Trials

State of the art clinical trials are largely computerized. For example, statistical programs and state of the art databases are used to store, manage, and analyze clinical trials data. Likewise, Electronic Patient-Reported Outcome systems (commonly known as ePRO systems) collect electronic information about the outcomes of the trials using proper questionnaires. The latter are filled by patients, clinicians, and the trial investigators in charge of the clinical trials. Nevertheless, the pipeline of clinical trial activities is far from being digital end-to-end. There are still several manual, non-automated and non-computerized activities such as filling in manual forms, doctor visits to report patient status and assess the progress of the trial, as well as manual design and monitoring of adherence to the trial protocol. The digitization of clinical trials aims at performing their end-to-end digital transformation, which will provide richer information, more timely progress monitoring of the trials, as well as a more accurate analysis of the results. In the scope of digital clinical trials, paper processes and manual steps are completely eliminated, in favor of fully digital processes.

The end-to-end digitalization of clinical trial activities is recently gaining momentum due to the rise of technologies that facilitate the collection, management and analysis of large amounts of digital data. For instance, cloud computing technologies facilitate trial stakeholders to access large amounts of storage and computing resources. Likewise, data mining and BigData analytics technologies enable the effective processing of clinical trials data, including the extraction of unique insights about the progress of the trial and the efficacy of the observed interventions.

Digital clinical trials provide all stakeholders with access to accurate and timely information about the status of the trial, including the need for any immediate actions. For example, the trial investigator can be instantly informed about a treatment’s side effects faced by patients. This allows the investigator to exclude patients from the evolution of the trial and replace them with others. As another example, the sponsor of the trial can be provided with real-time insights about the cost of the trial and the percentage of its completion. This is very useful for the sponsor’s financial and risk management processes.

 

The Role of the Internet of Things

Digital trials can be enhanced with more information when compared to conventional trials. This is made possible based on the introduction of Internet of Things (IoT) devices such as smartphones, smartwatches, and other wearables (e.g., bracelets or smart textiles). Such IoT devices enable the automatic collection of additional information about the patient’s status such as vital signs or daily activities information. The latter can be correlated with other pieces of digital information (like Patient-Reported Outcomes) in order to understand the impact of the drug or of the observed intervention on specific patients’ phenotypes. For example, the efficacy of a drug and the Quality of Life of a patient can be correlated with the lifestyle pattern of a patient. Moreover, the impact of daily activities and habits (e.g., nutritional habits) on the disease can be studied in a much more structured and systematic way.

Using IoT devices clinical trials stakeholders have more opportunities to monitor and ensure adherence to the clinical trial protocols. This is greatly facilitated by specialized IoT devices that get attached to “smart pillboxes” and provide information on whether the pillbox was opened and at what time. The recording of such information from smart pillboxes facilitates trial stakeholders to get accurate insights on whether the trial protocol has been respected by the patient. Overall, data from IoT devices can be seamlessly integrated in the pipeline of a fully digital clinical trial. Likewise, their information can be persisted in the cloud and analyzed using BigData analytics and machine learning algorithms.

The acquisition, management, and processing of integrated information about clinical trials can also provide the means for designing more efficient clinical research activities.  For example, it is possible to identify patient phenotypes for which a drug is more effective than others. This information could drive the inclusion of more patients of these phenotypes in the trial, as a means of maximizing the chance of drug approval, while minimizing the trial’s cost. Likewise, this approach could introduce the notion of adaptive and progressive clinical trials i.e. trials that do not have all their patients included upfront, but rather adapt to the identified phenotypes. The result could be better and more personalized drugs at very competitive clinical research costs.

 

The digital transformation of clinical trials has certainly a great potential for the pharmaceutical and biotechnology communities. Several companies are currently tapping on this potential by providing novel products for designing, executing and managing fully digital clinical trials. Some of these products focus on cloud management of clinical trials data, while others are focusing on applying analytics for extracting efficacy indicators. However, the digital clinical trials market is still in its infancy and its full potential will be unveiled in the years to come. Stay tuned!

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