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Leveraging AI to Design Optimized Clinical Trials

Conducting clinical trials is an essential aspect of carrying out business for pharmaceutical and healthcare companies. It is well-known that the entire process is also extremely expensive. It includes designing the scope of the trial, creating the inclusion/exclusion criteria for patient recruitment, managing data collection, performing analysis on data, and many other processes.

Each process has to be carefully executed and recorded to ensure that FDA submission is successful and the drug or treatment in question is approved for mass distribution. The entire process looks quite straightforward. The drug development journey should ideally follow this path:

The simplicity of the process graphic can lead you to believe that pharmaceutical companies should be coming up with life-changing medicines far more frequently than they do.

In truth, the reality is somewhat different:

Only a tiny percentage of the compounds or treatments undergoing a clinical study ever make it to the market. This is not due to a lack of resources because most companies assign almost half of their research budget to conduct these studies.

The large allocation of resources does not seem to make it easier to design clinical studies that are more successful. There are many fundamental challenges, most of which revolve around finding the right site for conducting the study and recruiting the right set of patients.

The time and the budget requirements of a clinical study need to be mapped out diligently. All the study planning processes must focus on ensuring that delays do not occur and the study does not go over budget. In a world where only a small percentage of trials are completed successfully, any solution that can reduce cost and cycle time becomes essential.

Let’s take a look at some key areas to focus on in order to ensure a study that is timely and within budget:

Site selection: With tighter regulations and shrinking budgets, it is necessary to have a strategy for selecting the most suitable sites for the study. A site’s access to a viable pool of patients, its infrastructure, and suitability for supporting the given therapeutic area of the trial, are among the crucial parameters for choosing a site.

Patient identification: Patient identification is a critical component of the clinical trial process. Finding viable patients for a study based on inclusion/exclusion criteria is an essential part of trial success. Less than half the patients screened for participation in clinical trial complete them1, which is why this area can experience significant delays.

Principal investigator: The principal investigator (PI) is the point of contact for CRAs (Clinical Research Associates) who would visit the site for pre-study approvals and assessments, and later on to verify data and keep a continuous check on the process. The PI ensures regulatory adherence, availability of the right infrastructure, and the correct execution of the protocol.

Running all these processes manually takes weeks to months of checking, cross-referencing, and writing reports. Even with the digitization of information, getting the right insight involves tweaking the parameters many times and comparing various reports and then verifying them. Artificial intelligence (AI) can indeed be used to counter this challenge.

By using machine learning capabilities, it is possible to quickly identify the most suitable patient pool, site, PI, and other components and run comparisons with historical data. Processes that used to take weeks to execute can be sped up to take only minutes or hours. Running various ‘what if’ scenarios can now be easy, quick, and accurate, as the analysis delivered is free of human errors.

To find out more about the ways AI can help trial feasibility prediction become more robust, read the white paper: Trial Feasibility Prediction: The Key to Designing Optimized Clinical Trials.

Tufts CSDD patient recruitment and retention 2.o

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About Leon Surgeon

mmLeon Surgeon has over 20 years of experience in the Software and Telecommunications industries. Results oriented product marketing/management leader skilled in profitably driving key areas of corporate business operations including the conception, strategy and implementation of a wide array of technology products and services.


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