14 Jun 2020

Updated 2 Nov 2021

The Future of Clinical Research: Big Data and Precision Medicine


The Future of Clinical Research: Big Data and Precision Medicine

– Takes about 10 minutes to read this topic –

Change is the only constant in clinical research. New data sources, new data types, and new trial designs that open up new opportunities for data analysis are emerging. These changes reflect a movement in clinical research toward the development of more targeted therapies.

As we embrace the concept of precision medicine, also referred to as individualized, or, in some cases (hyper-) personalized medicine, (bio- ) pharmaceutical drug developers are exploring new ways to evolve the development process so that treatments can be matched to the patients most likely to respond and potentially reach them sooner than traditional methods have allowed. [1],[2]

Despite great strides made over time, traditional approaches to clinical trials and drug approvals have proven slow, unreliable, and labor intensive as a result of the complexities of disease and the clinical evaluation of new therapies. The hope is that innovative approaches to study design and standardized electronic data submission will reduce the time and resources required to conduct effective clinical studies, accelerating the pace of biomedical research and drug development.

Using Data to Build Success

Since the advent of modern clinical trials in the mid-20th century, statistically driven studies have formed the core of evidence-based drug development. Data is one of the greatest assets allowing clinical trial teams to plan successful programs. Pre-clinical and clinical data is used to support trial design, decision making, and submission to regulatory bodies for marketing approval. But data is useful only if it meets specific standards and is properly managed and applied.

Moving Toward the Future

Leveraging gains made in DNA sequencing together with the ability to analyze big data has brought the promises of precision medicine closer. The application of statistical analysis methods has propelled even further retooling of the (bio-) pharmaceutical industry, and statistics are now recognized as a cornerstone of evidence-based clinical research.

The dawn of the 21st century has brought a number of challenges to drug developers and the (bio-) pharmaceutical industry. Traditionally focused on developing blockbuster drugs at large scale, drug developers are now motivated by an advanced understanding of the biology of disease along with changing technologies and regulatory requirements that support the development and delivery of drugs that fit the individual patient’s biology and pathophysiology.

This change from the blockbuster strategy to precision medicine will, to a large extent, influence the way that drugs will be developed, marketed, and prescribed in the future, imposing major changes to corporate structures as well as the design of clinical trials, the regulatory frameworks, and oversight requirements.

Traditional clinical trials work well if the objective is to understand whether one treatment is better, on average, than another. But with a shift toward treating the individual, average outcomes hold less significance, because no single person exactly fits the average model.

Developing novel drugs to treat subgroups of patients who may respond to one treatment over another because of genetic or other underlying factors has led to smaller, more flexible clinical trial designs. These trials focus on clearing specific hurdles sooner while pairing the right patients with the right drugs.

Enabling Technologies

Precision medicine is a clear target of current drug development. To support this approach innovative trial design and enabling technologies are required. The eClinical trial model—which uses information systems to collect, access, exchange, and archive the data needed to manage, analyze, and report trial results—has led to the emergence of new tools for planning and conducting trials. These tools include clinical trial management systems (CTMS), electronic data capture (EDC), and interactive web response systems (IWRS), among others. To be successful, clinical trial teams and drug developers need to have a thorough understanding of the design of trials and the tools that support them.

Technology will continue to provide a strong foundation to optimize the use of big data in innovative new trial designs. This foundation connects big data and innovative trial designs to novel therapies of the future. Technology solution providers must maintain the rapid pace of innovation while also preserving their commitment to minimizing risk and maximizing security, efficiency, and compliance.

XClinical’s eClinical solutions deliver powerful functionality that facilitates the entire clinical trial lifecycle.  Our technology solutions are supported by fast, focused and flexible professional services that enhance the use of our cutting-edge solutions.

Contact EvidentIQ today to see how we can help you manage your next clinical trial and protect your valuable clinical data.

[1] Ginsburg GS, Phillips KA. “Precision Medicine: From Science to Value.” Health Aff (Millwood). 2018 May;37(5):694-701. doi: 10.1377/hlthaff.2017.1624.[2] National Research Council, Committee on a Framework for Developing a New Taxonomy of Disease. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. National Academies Press; Washington DC: 2011.

Image Credit: Michał Parzuchowski on Unsplash

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