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What Are the Main Challenges of Modern Clinical Trials?

BioSpectrum Asia

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BioSpectrum Asia May2024

With a cumulative total of almost 478,000 globally registered trials from the year 2000 to 2023, and growing at around 10 per cent a year in the last five years, the pharmaceutical industry continues to invest in clinical trials as the gold-standard test of therapeutic safety and efficacy. In addition, as the world’s economies and populations move in new directions, the share of non-US trials has reached 54 per cent, a trend that is likely to continue, driven by demographics and epidemiology. As the pace accelerates, trial managers and clinicians face a significant challenge: the explosive growth of data volumes. To succeed in the future, clinical trials need to increase efficiency and better utilise clinical trial data to support informed clinical and operational decision-making.

What Are the Main Challenges of Modern Clinical Trials?

While the increasing volume of clinical data is welcome in many respects, clinicians and trial managers often struggle with timely access to data held in multiple repositories, and face issues around quality, complexity, and format. To address the problem, the biopharmaceutical sector has invested large sums in data warehouses and data lakes, but timely access to analytics-ready data remains a serious challenge for non-specialists.

Some of the difficulties themselves stem from prior approaches. Manual solutions for data extraction, such as custom programs or scripts, can be costly to develop, deploy and maintain. A network of point-to-point data exchange services soon becomes inordinately complex as new sources are added, and the lack of centralised management can hinder or prevent team collaboration and efficiency.

For example, a cross-study analytics project to review drug safety information across a portfolio of oncology products would naturally need access to clinical data from multiple trials. The combination of different protocols, cohorts, data gathering, and formats soon leads to ballooning complexity as each trial is added to the project.

Solving or reducing data management challenges would immediately release time for better and deeper analytics, supporting the true purpose of a clinical trial, to discover the efficacy and efficiency of new therapies. Ultimately, the less time spent on wrangling data and the more time spent on investigation, the better.

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