Clinical Data Management and How It Can Benefit Pharmaceutical Companies?
FAIR Data

Clinical Data Management and How It Can Benefit Pharmaceutical Companies?

Jayashree
September 8, 2022

The world woke up to the importance of clinical trials in the aftermath of the Covid-19 pandemic. Nearly 5,000 clinical trials were launched in 2020 and 2021 to test life-saving treatments and vaccines for the novel coronavirus. Clinical data plays a key role and offers critical proof of a drug’s safety and efficacy. With massive amounts of clinical trial data being generated, it is imperative to use efficient data-capture tools to ensure high-quality data for accurate drug evaluation and to accelerate the process of drug discovery and development. The increasing demands in the current global drug market are pushing pharmaceutical companies to adopt innovative ways to minimize drug development timelines and improve productivity.

What is Clinical Data Management?  

It is defined as the process of cleaning and managing the data generated during clinical trials. The goal here is to maintain high-quality, reliable, statistically-sound research data that is in accordance with regulatory guidelines and standards. In this blog, we talk about the pain points for pharmaceutical companies with respect to clinical data and how clinical data management can benefit them.

Data, Data, and More Data

The global clinical trials market size is expected to reach USD 78.3 billion by 2030, registering a CAGR of 5.8% during the forecast period, 2020-2030.

Global Clinical Trials Market (Clinical trials market size, share & trends analysis report, 2020-2027)

Current Challenges Faced by Pharmaceutical Companies

Major challenges include:

  1. Managing massive amounts of trial data.
  2. Keeping up with stringent quality assurance regulations.
  3. Coping with time and resource-intensive drug development process.
  4. Handling trial data prudently to avoid data breaching.

Future of Clinical Data:

We are in the era of patient-centric precision medicine.

Precision medicine classifies patients into subpopulations based on differences in demographics, genetics, prognosis, susceptibility, treatment response, and other essential parameters.

Many leading pharma companies have redesigned data collection processes and are beginning to focus on creating a continuous learning algorithm involving clinical trials and real-world experience. The enormous amount of clinical data in hand can be collected, organized, and extracted to facilitate the search retrospectively for benchmarks that can be leveraged to improve care and also to assess how different sets of people have reacted over several years.  The aim here is to demonstrate whether some treatments work more effectively in some subpopulations than others.

We are gaining real-world data (RWD) from patients for claims data, genomic data sets, electronic health records (EHRs), electronic medical records (EMRs), imaging systems, mobile apps, personal health records (PHRs), and wearables. The growth in data has given rise to a subindustry of established companies specializing in RWD aggregation. These third-party RWD aggregators can be leveraged to help shift to the new paradigm of data-driven clinical trials based on precision medicine. Precision medicine can quicken development timelines and enhance R&D productivity as it becomes embedded in the clinical trial and drug development processes.

To support this approach, innovative trial design and enabling technologies are required. Regulatory bodies such as Food and Drug Administration (FDA) are also implementing guidelines to push companies to switch from pen and spreadsheet data collection methods to electronic data storage making Clinical Data Management Systems (CDMS) the standard method for data submission. Software tools used in clinical data management are CDMS, ORACLE CLINICAL, and CLINTRIAL to name a few.

Clinical Data Management: Fool-Proof Execution of Data Generation

The pharmaceutical industry (including clinical trials and clinical data management) is going through a profound transformation. The CDMS Market size accounted for USD 1,996.6 million in 2021 and is estimated to grow at 11.4% CAGR between 2022 and 2030. As a result, the companies transitioning to better clinical data management platforms will have a competitive edge over the ones that don’t and will phase out the latter in the long run. Maintaining a database starting from Phase 1 of trial to post-marketing takes up to a decade and can cost billions of dollars. Pharmaceutical companies have their eyes set on emerging start-ups that offer platforms to leverage trial planning and execution as a long-term procedure to increase revenue. Some of the notable players operating in the global CDMS market are eClinical Solutions LLC, CIMS Global, Axiom Real-Time Metrics, IBM Watson Health, and Veeva System. They have implemented various strategies like collaborations, new product launches, and mergers to capture a high revenue share in the CDMS market. Clinical data management is taking us into new spheres of decision-making with the potential to transform diagnosis and treatment and it is high time that more and more companies adapt to this approach and streamline their processes.

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