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Medicine is evolving, and sample management must evolve with it.

The Future of Sample Management for Clinical Trials

Best practices for integrating digitalization into sample management workflows

Tola Olorunnisola

Tola Olorunnisola is vice president of innovation, marketing, & digital at Avantor.

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,Bruce Dembofsky

Bruce Dembofsky is director, innovations and partnerships at Avantor.

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Published:Oct 11, 2022
|4 min read
Photo of tola olorunnisola and bruce dembofsky.
Tola Olorunnisola (left) is vice president of innovation, marketing & digital at Avantor. Bruce Dembofsky (right) is director, innovations and partnerships at Avantor.

Samples are at the core of every life-changing therapy, making them invaluable and irreplaceable research assets. As the biopharmaceutical industry is being challenged to produce effective first-to-market treatments faster than ever, sample management has become critical in meeting the rapidly evolving needs of everyone from clinical trial managers to patients. 

The challenges of sample management

In June 2022, more than 418,000 clinical studies were registered in 220 countries, including the United States. In many of these trials, researchers contend with roadblocks that make it difficult for them to efficiently manage biological and nonbiological samples, including the following:

  • Lack of sample visibility: Sample management has often been a patchwork of technology solutions, creating persistent fragmentation in information and process workflows. Other factors, including mergers, acquisitions, and personnel changes, can also prevent researchers from fully understanding where their samples are stored.  
  • Rising costs: R&D costs in the pharmaceutical industry get transferred to the patient. As drugs become increasingly difficult to discover, the costs to develop them and run clinical trials increase as well. Taking inefficiencies out of the sample management process can help lower cost and increase speed to market. 
  • Supply chain issues: From temperature breaches to lost samples, supply chain issues are among the most common causes for sample integrity failure. 
  • Increased risk of error: Large pharmaceutical companies run hundreds of clinical trials, each involving thousands of trial managers and teams. Often, each trial’s sample management is supported by a different third-party vendor that uses its own processes and technologies. Each touchpoint creates more opportunities for errors, such as lost samples, research delays, and poor patient experience. 

Successfully overcoming these challenges will require clinical trials sponsors, contract research organizations (CROs), and their partners to shift mindsets and adopt technological solutions that can help remove these roadblocks.

Bringing consumer-level transparency to sample management

Trust in clinical research begins with transparency. All stakeholders, including patients, researchers, and clinical trial managers, can benefit from insight into how their samples are traversing the supply chain: Where is the sample? When will it arrive? How is it being stored? 

Consumer-level transparency also helps support the complexities of sample management in decentralized clinical trials, especially when trials must collect biological samples from patients’ homes. 

To achieve such transparency and ensure sample integrity, technological solutions can help. For example, a kit with a scannable label can track the full lifecycle of a sample, from a patient’s home to a clinical lab then a biorepository. Radio frequency identification (RFID) chips can also be embedded in kit components to track patient compliance and help ensure a sample’s integrity. 

Tube etching can also help identify samples even if a barcode label becomes unreadable. Machine learning tools can also enhance transparency by taking  factors like capacity or service hours into account to provide dynamic, multi-mode estimated-time-of-arrival information. 

These technological capabilities ultimately create the tracking and traceability that enhance the patient experience and assist clinical labs with the documentation and rigor needed to meet the regulatory demands of GDPR in Europe, HIPAA in the United States, and other regulatory bodies.  

Harmonizing clinical data across the sample ecosystem

Data is an increasingly important part of sample management, especially for personalized therapies that require documenting chain of identity and chain of custody. Along with data collected from the physical sample, other valuable research assets can be collected too, including the sample source, the indication and the drug administered, as well as informed consent information.

Sample management can consolidate data inventory to ensure regulated data can be tracked and traced across the entire ecosystem. This capability allows researchers and clinical trial managers to quickly access critical information, including study reports, raw data, and clinical trial master file records. These data systems must be compliant with HIPAA and GDPR, whether the lab contracts a third-party vendor or stores digital sample information itself.

Properly managed data can also be leveraged for emerging digital twin technology, which uses a patient’s longitudinal clinical record to develop a model of how the patient’s condition would evolve if given a placebo. 

Improving clinical trial research through enhanced sample management

As part of a holistic sample management strategy, digitalization can help clinical professionals quickly and efficiently structure new drug development trials. Scaling novel supply chain technology and related infrastructure will eventually allow us to manage the full data picture and map the journey of a single sample across multiple vendors. Such technology can also potentially help researchers determine whether a sample collected for one indication could also be used for others. Sharing these data across all trial inventories would create exciting possibilities for future trials.  

The long-term storage of physical and digital samples is a key factor in ensuring the integrity of not only active trials but also trials investigating emerging therapies. Researchers with access to effectively managed, compliant data, including consent information, can more efficiently repurpose the sample to quickly initiate new trials.

Best practices for integrating digitalization into sample management workflows

  1. Consider sample management from the start. Sample management doesn’t begin with long-term storage. Take a holistic approach that begins during the study planning stage so that you can efficiently manage samples for the active trial and beyond.   
  2. Involve IT team members. Avoid potential disruptions by consulting IT personnel with the expertise to help mitigate problems earlier rather than later.  
  3. Use open architecture systems. Proprietary technology will harm your flexibility and ability to scale. Choose vendor-agnostic technology that creates interoperability with current and future technologies. 
  4. Identify partners with digitalization expertise. Optimize sample management by working with experienced third-party vendors and subject matter experts. By working together toward more open communications and consistent data formatting protocols, you enable the real-time data exchange that provides visibility across the full sample lifecycle. 

Medicine is evolving, and sample management must evolve with it. Just as researchers and clinical trial managers now use advanced technology like artificial intelligence to feed research models, they can also leverage digitalization to manage the entire lifecycle of samples and bring life-changing therapies to market faster.