Reimagining Drug Discovery with Smart Clinical Trials

Published Date

March 11, 2019


3 minutes

Written By

Debdutta Banerjee

In Contempt of various potential advantages offered by way of modern technologies and capacities, the biopharmaceutical organization has commonly been slow to digitize its clinical trial process. Many biopharma corporations nowadays think about digital as a set of technology, structures, and superior analytics, which may include connected devices, mobile applications, artificial intelligence (AI), and robotics. The first-moving organization is also typically focusing on the fragmented solution and piloting technologies in exclusive areas of clinical trial and development to aid the existing scenario.

Biopharma companies can get significant advancement through Digital Innovation called “SMART CLINICAL TRIAL”. This will assist to reduce the cost of trial management as well as time and help to get patient-focused endpoints and product value proposition.

Digital advancement can expand the measurable power and affectability of Clinical Trial

Progressively visit information gathering (day by day, hourly, constantly) utilizing sensors and wearables can produce considerably more health data than occasional standard clinical assessment. Thus, a treatment’s impact can be exhibited with shorter duration and limited patients, requiring less exertion and cost for enlistment and maintenance.

Advanced technology can capture quietly focused endpoints of the treatment and reinforce value proposition

Wearable devices and mobile technologies are making clinical trial smarter and easier actual patient outcomes without intervention in patients’ quality of life. The new technologies that seamlessly collect patient-generated data —enabling the opportunity to attain “ Digital Endpoints” and offer the possibility to achieve a trial decision in shorter time, which cloud save time and money without sacrificing data quality.

A pharmaceutical company trial the feasibility of using mobile devices to collect data on endpoints that matter to rheumatoid arthritis patients, such as joint pain and fatigue. This involved creating an app that gathers data from surveys and smartphone sensors. In the morning, a patient answers questions about the degree of joint stiffness and other metrics. In parallel, the phone’s accelerometer records data from wrist motion exercises. The study found that raw accelerometer data could be converted into a score that was much more precise than motion-scoring exercises conducted in a physician’s office. (Reference from Nick Paul Taylor, “GSK starts RA trial on Apple Research-Kit)

Rethink and Reshape: let’s extract the new insights from existing data through digital technologies

Now Clinical trial Organizations or Biopharma companies are started using Artificial Intelligence and advanced analytics to assemble the data from different sources like real-time data, claim submission, health records, completed and ongoing research which can give a wide-angle view of the evidence. This may help to improve the further process can suggest new indication, can surface the safety issues and help to solidify the assumptions about the drug and its success in a trial in the early stage.
For example, Trial Automation of Low-level repetitive work like creating standardize contract draft, patient data point capture, quality check for missing data, reconciliation of entire inputs, natural language processing, populating a standard study report etc, can reduce the compliance risk and go-live time to enter into the market.

Reinvention with new technology, Clinical Trial can be more productive

Feeding data into different CRO systems like Electronic data capture (EDC) system to their own portal, the adverse reporting system is reducing the productivity of the Investigator. Automation can aid the burden by digitizing standard data and allowing the data exchange across the myriad of silos. This will increase productivity and reduce human error significantly.

Optimize site performance and enable site support

Centralize analytics platforms can assemble and visualize data from multiple sites and generate more precise results faster via centralized monitoring, and provide a real-time view of site performance, Improve data quality and productivity. Intellectually Machine Learning and natural language processing can analyze performance statistics, satisfy site based totally on their productiveness, monitor the undesired results. This will help sponsors, CROs and other stakeholders saving their millions of dollars.

There are financial, operational, cultural, and statistics accessibility/interoperability demanding situations to adopting a virtual R&D method, however doing so may be no longer only a scientific necessity; it may be an enterprise imperative that could enhance biopharma companies’ potential to have interaction, innovate, and execute during the clinical trial process. Organizations are needed to invest in new capabilities, skill sets, and partnerships in return. They could receive the benefits of a considerably more proficient R&D process, enhancing both the nature of advantages and the time and cost it takes to inspire them to market.

About the Author

Debdutta Banerjee

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