What Are The Most Prevalent Challenges around Biospecimen Data & Operations? A Q&A with Slope’s Mandy Schreiber
Slope recently welcomed Amanda Schreiber as the newest addition to our Biospecimen Data and Operations team. Amanda brings a wealth of experience to the team, having worked in various capacities with sponsors, research sites, labs, CROs, and other vendors over the course of her expansive career.
We sat down with Amanda to talk about all things pertaining to biospecimen data and operations — from research site compliance to sample tracking and data reconciliation.
You’ve been in the clinical research space for a while, and in that time you have seen a lot and have worn a lot of different hats. What are some of the most prevalent challenges you've noticed, both with sample management and biospecimen data management?
I’ve been fortunate to gain experience in various areas of clinical research, starting at the site level. I worked in the clinic for a couple of years before moving to the data side, so I’ve seen things from both perspectives. I’ve also worked at a vendor and a CRO. While great things are happening at all levels, there are gaps as well.
At the site level, inventory tracking was a major issue. There was one large storage room where all the kits were thrown together — oncology kits mixed with dermatology kits, for example. Pulling the correct kit and prepping it for clinic days took a lot of time. We used an outdated Excel document for inventory, and when kits were nearing expiration, we often didn’t know until it was too late. This led to using site-sourced tubes, which aren’t acceptable for today’s increasingly specific assays.
Time constraints were another challenge. Even small data discrepancies could cause delays in testing, shipping, and increased costs. Errors in manually completing paper requisitions were often not realized until received by the lab, which had downstream effects on patients, clinic schedules, and staff. It was mind-blowing that we couldn’t automate processes to prevent these issues.
Given that clinical research occurs in an ecosystem of disconnected stakeholders (sites, labs, and other vendors), coupled with the fact that the industry relies on different systems — like EDC and LIMS — what do you see as the most prevalent challenges with data reconciliation?
That’s a big issue, to be honest, because it takes a lot of time. When you have various different labs, you're going to get different types of output. As much as we try to streamline the format of data, some labs’ LIMS systems just can’t do that. It’s a very manual process. And when you have a very manual reconciliation process, it’s not only labor-intensive but also prone to mistakes due to human error. Even with the best intentions, human error is inevitably going to happen.
When the scale or complexity of the study increases — early-phase oncology trials or Phase 2/3 studies with thousands of patients — each data point requires its own reconciliation. For each visit and for each cohort, it’s very labor-intensive. As I mentioned, mistakes will inevitably happen. So, when a sponsor suddenly says, "The FDA reached out," or "We’re looking at an interim analysis based on biomarker results," you must ensure the data is clean to at least a Level 2 standard.
If you’re handling multiple trials and thousands of time points, even with advance notice, getting everything to Level 2 is near impossible when it’s that manual. Now, when you remove some of that manual process through automation, it cuts down on human errors, and the timeline for getting data to Level 2 for interim analyses, abstract submissions, etc., becomes far more reasonable. The data quality is at a much higher standard within a smaller time frame.
When you talk about getting biospecimen data to a "level 2 standard," what does that entail? How clean does the data need to be to meet that?
Level 2 means the data has at least gone through one round of reconciliation, with discrepancies identified. Level 3 is "as is" data — we can't speak to whether or not there are issues or discrepancies. Level 1 data is usually for database lock; it's gone through reconciliation multiple times and is as clean as possible. Level 2 is in between. When sponsors present an abstract on a study or determine a need for an interim analysis, they'll often require certain data points to be at a level 2 standard — usually biospecimens like ctDNA or genetic biomarkers. That means someone has reconciled the data at least once, identified discrepancies, and handed a list of those discrepancies to the scientists analyzing the outputs..
In my previous roles, we would compare EDC data with a CSV file of the result data, run them through a SAS program, and it would shoot out discrepancies. I’d go line by line, identify discrepancies, query the site, query the vendor, and note any unresolved issues in a spreadsheet for the scientists. That’s level 2 data — someone has reviewed it and identified where the issues are, but they haven’t necessarily been fixed yet.
When you say that automation can reduce the need for reconciliation, can that come in the form of reducing duplicative data entry?
Absolutely. From my experience working at the site level, we had to fill out requisitions, enter that data into the EDC, and then, when samples were received by the central or testing labs, they would also accession the samples and enter the data into their LIMS. Lab personnel cross-check it with what I already input into the EDC, potentially leading to multiple queries. Not only did I have to duplicate my efforts, I had to do it 3, 4, or even 5 times, depending on how many labs we’re using. It’s a lot of duplicative work.
It also feels like the game of telephone — at every stage of duplicative work, the risk of one minor mistake increases. By the time the data is released, due to these duplications, we can't always guarantee that the data is at the level of integrity we want, requiring us to go back and manually reconcile it. So yes, it’s a lot of rework.
It’s no secret that we have to reconcile data in clinical research because of issues like a lack of data standardization, duplicative data entry, and human error. What’s your perspective on why site compliance with sample metadata capture is a problem? How does that manifest itself in the form of queries?
There are a couple of things that factor into it. At the site level, the focus is understandably on the patients in the clinic. When I worked at the site level, I might have had fifty queries to resolve, but I also had six patients in the clinic that day. On top of that, I was the only person doing all the data entry, sample shipment, and query resolution for multiple global Phase I trials. With so many responsibilities, queries were often the last priority. If I made a mistake while rushing to enter data, I would think, "At least I got the data in," and move on to patient care. It could take days before I had time to clean up those queries.
Many sponsors are eager to run large global trials, but sites don’t always have the infrastructure to support them — primarily when it comes to data integrity. Sites focus heavily on the patient-facing side, with data taking a bit of a back seat. However, without clean data, the trial loses its purpose.
In your experience, what does the process for sample tracking currently look like for most sponsors? What are some of the challenges there?
Some sponsors use internal systems where sites input basic tracking information, but these are often inaccurate and don't integrate with other systems. For example, if I were to click on a tracking number, I would still have to manually check the shipment’s progress and follow up the next day. I wouldn’t receive notifications about delivery or transit issues, making it difficult to stay informed.
Other sponsors rely on manual methods where site coordinators email shipment information, but critical details like sample types, shipping temperatures, or whether all required samples are included are often missing. This lack of visibility makes it hard to ensure compliance, and by the time we realize something is missing, it’s often too late.
Another issue is shipment stability. If ambient samples are delayed, they can fall out of stability, impacting patient care. The manual nature of tracking — copying tracking numbers into courier websites — creates visibility struggles and increases the chances of error.
At my previous company, we relied on site coordinators’ emails to inform us of shipments, followed by constant refreshing of lab reports to confirm receipt and proper accessioning. This reactive process wastes time and increases the risk of lost samples or delays.
With a solution like Biospecimen360™, sponsors can be proactive. Accessioning teams can anticipate shipments and prepare in advance, improving transparency, reducing costs and delays, and lessening the burden on patients and physicians.
Have you seen any examples where a lapse in sample tracking led to a negative outcome for a patient?
When I worked at a site, we had a patient in an early-phase oncology trial that required a bone marrow biopsy. These are very invasive and painful. Many patients may refuse to participate just because of the biopsy requirement. We collected the biopsy and shipped it the same day. Due to the sample being ambient, it required accessioning and storing immediately upon receipt. Without lab visibility of the incoming and expected shipment, personnel were not present to receive the sample. Unfortunately the sample was not accessioned or stored properly upon receipt and fell out of stability.
If we had been able to streamline the process for proactively informing the lab of the incoming bone marrow sample, personnel could have been allocated to be present to receive it. Unfortunately, this resulted in an unnecessary and painful duplicate biopsy for the patient, frustration for the treating physician, and financial burden and extended timelines for the sponsor.
Sample tracking still has a long way to go, but Slope is leading the way. They’re removing the manual aspects and automating so much of it, which is really a game-changer.
To learn more about how Mandy and the rest of Slope’s experts can support your study with our tech-enabled services, be sure to check out our full list of Professional Services.
