Are Your Clinical Trials Equipped For A Modern World?

As clinical trials become more complicated, that’s why a clinical research organization like Veristat and even sponsors are under increasing pressure to satisfy speed and efficiency targets. There is a need to improve key endpoints such as inclusion, decentralization, and help find treatment for rare diseases. This means trials must be fully equipped to adapt to future changes if they are to be successful.

The state of traditional trials

When a patient comes to the clinic for check-ins, the study investigator double-checks their pill bottle for any remaining tablets and goes over their diaries for any gaps or inconsistencies. The investigator depends on the patient’s memory of events if the diary entries are missing information.  Paper documents are an old and inaccurate approach to capturing essential data points for clinical trials since they can be misplaced or lack key information. The trial may exclude additional MRI and lab tests performed during follow-up visits. Since the tests are for research objectives rather than medical needs, health insurance may not fund them. All these methods are outdated and unreliable which results in high chances of patients dropping out.

How modernization is changing clinical trials

In the last few years, the pandemic heightened the need to make trials timely and more efficient. For that, massive changes have been made to this effect. These changes include;

Remote monitoring

Several monitoring devices and sensors are being developed, with machine learning being used to analyze the data, to enable remote patient monitoring for clinical studies. Such devices can help with studying drug adherence in real-time. They provide reminders, educational information, dose tracking, and patient-reported data capabilities for providers through a corresponding mobile application. Physiological data, such as aberrant cardiac rhythms, can also be captured by other equipment.

Adaptive study design

Adaptive design is a method of running a trial that allows for additional flexibility. It enables researchers to change the trial’s major endpoints as it develops. This prevents resource waste and delays in the timeline. Open research forums are used in an adaptable form to speed up prospective results and conclusions, increasing collaboration in crucial research fields like cardiovascular complications. As alternative techniques to established study design practices are questioned, these developments might create opportunities for researchers to learn.

Virtual trials

Interest in virtual clinical trials has grown as telemedicine and remote monitoring techniques have become more widely adopted. The goal of virtual trials is to make studies more accessible to patients by utilizing a network of investigators, mobile nurses, and study coordinators. Though decentralization may not be ideal for all trials such as those that require in-person assessment, this strategy offers a way to encourage patient participation and experience. Further, virtual trials provide reduce operating costs and more patient acquisition and retention.

What to Expect in Future

Pharmaceutical industries are striving to use AI-enabled analytics and automation to design clinical trials that can be completed faster, more efficiently, and with better patient results. Pharmaceutical businesses can make better judgments throughout the clinical trial design lifecycle if they have access to insights from historical clinical trial data and real-world evidence. As a result, future studies will rely heavily on technology in terms of data collection.

Data ingestion pipelines will be used for analytics and insights, ingest historical clinical trial data, publically available datasets, and real-world data.  The human-centered design will allow users to build, compare, and optimize trial design scenarios based on findings from previous trials. Natural Language Processing (NLP) pipelines will enable the extraction of key items from clinical trial documentation. While the deployment of the AI model will enable the prediction of the influence of trial design decisions on downstream metrics such as costs and quality risk.

As the clinical trial world evolves, CROs should ensure their trial processes and tools evolve as well. In doing so, the process of drug development will be reduced ensuring fast availability of drugs.

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