Clinical trials are the way to get drugs and treatments approved and in the mass market. However, the entire process is very expensive and cumbersome. Sagar Anisingaraju discusses the challenges and the possible solutions. His article further explores ways to optimize clinical trials with machine learning and RPA.
High cost and low success rates are the two top issues that plague clinical trials. The pharmaceutical industry has turned to technology to combat these challenges. New data analytics solutions are emerging everyday that promise to deliver data insights quickly, efficiently, and accurately. With better analytics and improved processes, such as centralized monitoring and automation, the industry is hoping to bring down the cost of conducting clinical trials and research, so that new treatments can get to market faster.
Clinical trials are critical for the pharmaceutical industry, because no new treatment or drug can come to market unless it has been put through a trial and been approved. A testament to their importance is the fact that only 10 percent of all clinical trials reach the approval stage. However, while this practice ensures safety and efficacy, it also makes the entire process of getting a new treatment or drug into the market very expensive.
For the successful completion of a clinical study, at least the following factors need to be in place:
- Regulatory compliance
- Pool of willing subjects and patient recruitment
- Procedures to monitor safety of patients and to ensure high quality of data
- Appointment of the most suitable principal investigators
- Proper execution of the clinical study design
Each of these steps needs careful planning and execution. The compliance process is lengthy and precise; protocols are increasingly becoming more complex, and the monitoring, collection and cleaning of data is very expensive. Identifying, recruiting and ultimately retaining subjects is another challenge altogether.
Let’s look at a typical clinical research process:
It is obvious that the high cost of conducting clinical trials is directly proportional to the time it takes to execute the trial. To quote my article on optimizing clinical trials:
“At today’s high cost of conducting RCTs, extending a study timeline by as little as a month can result in significant budget overruns, not to mention the potential revenue and opportunity loss from delayed drug commercialization.”
Each phase of the clinical trial needs to be planned and executed perfectly and the results submitted for approval to a regulatory body. Collating, verifying, and analyzing the data collected in the trial is extremely vital and the entire process is time-consuming.
The Data Management Process
With today’s technological solutions, it is possible to manage data efficiently with the help of cognitive systems in order to optimize clinical trials. Read all about how cognitive computing can change the way clinical research is conducted by making use of machine learning and RPA (Robotic Process Automation) in Optimizing Clinical Trials with Machine Learning & RPA in Genetic Engineering & Biotechnology News.