Technologies Rescuing Pharma Companies from Clinical Trial Failure

Pharmaceutical companies involved in clinical trials are gradually considering modern technologies as ultimate solution to avoid clinical trial failures.

Clinical trials are costly and time-consuming processes. Its failure can cause significant revenue losses for pharma companies. According to Tufts Center for the Study of Drug Development, the estimated average cost for the development of new drug until it reaches market is somewhere around US $2.6 billion. In addition, many other reports suggest that it takes over 13 years to launch a safe and effective drug.

In order to accelerate and streamline the slow and exorbitant clinical trial process, the clinical trial industry is trying to integrate medical trials with innovative technologies such as Cloud Technology, Artificial Intelligence, Big Data Analytics, Predictive and Prescriptive Analytics, Internet of Medical Things and Wearables and Mobile Apps.

Cloud Technology: The success of clinical trial is also attributed to appropriate clinical trial design. The design includes selection and measurement of variables, size of sample, inclusive and exclusive criteria and data analysis approach.

This process is quite complex and cloud technology in this regard is helpful in simplifying the entire process. The technology provides required information about sources and regulatory guidelines at least cost and in rapid pace.

The specific features like cost effectiveness and flexibility in cloud technology well supports clinical data management systems. It provides better electronic connectivity, accessibility, real-time monitoring, collaboration and exchange of information.

Artificial Intelligence (AI): Leveraging AI in clinical trial can automate patient recruitment and medication adherence in least possible time.

Company named Deep 6 AI is one of the best examples of AI used by TD2—oncology-based drug development organization. It (Deep 6 AI) uses patient-trial matching platform in order to compare patients, cohorts and populations at high speed which significantly reduces trial time.

Big Data Analytics: Big Data Analytics are beneficial in simplifying voluminous complex data. This helps in finding the right patient for the treatment in trial process.

Predictive and Prescriptive Analytics: Converting acquired data into valuable information refers to predictive analytics whereas selection of best decision is known as prescriptive analytics. These technologies are beneficial in predicting the outcomes of trial. On the basis of such outcomes, stakeholders make rational decisions like whether or not to continue the trial.

Internet of Medical Things (IoMT) and Wearables: The use of IoMT and wearables in clinical trials help to collect new endpoints and additional data. It helps in regulatory filing or reimbursement. Using such devices help to reach large mass without visiting site physically for both—physicians and patients. It also offers remote monitoring solutions at least cost.

Mobile Apps: This technology is helpful in managing trial activities because with the use of mobile apps researchers can collect and interpret data from appropriate source. It leverages trials with remote monitoring, diagnostic, treatment and outcome of the treatment.


Subarna Poudel is a researcher with Frost & Sullivan. He can be reached at subarna.poudel@frost.com


Sapan Agarwal drives content and marketing for Frost & Sullivan. Sapan is based out of Kuala Lumpur Malaysia and can be reached at sapan.agarwal@frost.com | +603 6204 5830

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