Why Traditional Data Loggers May No Longer Meet FDA Regulations for Pharmaceutical Shipments

Why Traditional Data Loggers May No Longer Meet FDA Regulations for Pharmaceutical Shipments

Written by Modality Solutions

Posted on: March 20, 2019

FDA-Regulations

Temperature data loggers (also referred to as data loggers or temperature loggers) have been around for decades and were one of the first sensors in the biopharmaceutical cold chain. This early technology provided temperature audit trails of shipments in transit, enabled partners in the cold chain to confirm whether the products remained within specification throughout transit, and verified the acceptability of the product for the next partner in the cold chain.
 
Unfortunately, most temperature data loggers utilized in the industry stopped development at this limited functionality years ago and have not kept pace with the needs of the modern pharmaceutical cold chain. The strength of any cold chain lies both in its individual links and in all of the connections between them. As manufacturers, wholesalers, distributors, and retailers increasingly require environmental and product movement visibility, the limitations of data loggers become transparent.
 
Many data loggers are on the market today for temperature-controlled shipments of perishable biopharmaceutical products. Their latest advances allow ‘real-time’ data monitoring capabilities, yet this technology can often fall short of reporting accurate data at all steps in the cold chain and impedes the ability of all partners in the biopharmaceutical cold chain from capturing the full visibility, scalability, and agility necessary to support the growth of the environmentally-sensitive biopharmaceutical therapy revolution underway.
 
The status quo technology requires significant manual input: data collection must start and stop through a data logger. If this operation is performed incorrectly, the shipment data may not be collected at all unless next generation shadow monitoring is available. If the material handler activated the data logger at the initiation of transport, the receiving end completes the process by uploading the data and completing an ad hoc shipment review. However, if the data logger is incorrectly placed by the material handler before the product is transported, the data collected would not be valid for the shipment per the qualification requirements of the shipping system. All these steps in the cold chain increase the likelihood of human error occurring along multiple cold chain touchpoints, potentially delaying the review process.
 
The focus has been on temperature logger technology, the hardware, and the data collection software that powers them, not on visualizing the data and proactively planning your cold chain. Status quo hardware and software are not empowering businesses to fully leverage the opportunities that the explosion of ‘big data’, ‘machine learning’ and artificial intelligence (AI) really presents, including predictive and prescriptive analytics and proactive response to prevent excursions from happening at all.
 

Is there a better way?
Most data loggers require the physical collection of data after a trip’s completion. Storage memory tracks temperature data captured at regular intervals, typically every 15, 30, or 60 minutes. After a delivery, data is extracted by manually plugging in a USB cable to the logger or other means and software reads and plots temperature (and possibly other environmental conditions) data, then notifies the receiver as to whether the products have met the required quality and compliance requirements.

Extracting data from loggers can take between 15 to 30 minutes per logger, and as previously described is a highly manual process. If the data shows a possible excursion, the products enter into a quality review process, which may take up to 24 business hours or longer to verify. Without a complete data set, whether ‘real-time’ or not, the biopharmaceutical cold chain is not able to ensure compliance. Visibility is worse than ad hoc: it is incomplete.

At the end of a shipment, loggers are discarded or returned, and even if they are meant to be returned, without logistics processes in place, they may be discarded anyway, adding to the total waste attributable to a shipment.
 

 

Why do we need data loggers at all?
Less than a decade ago, the pharmaceutical industry believed it was not realistic to expect end-to-end visibility into shipment locations and statuses across an entire supply chain. Today, it’s not only possible and readily available, but there are cost-effective ways to achieve it. But is it enough?
 
At first, ‘real-time’ data loggers were prohibitively expensive and used only for a portion of the supply chain, either for routes that have historically posed challenges, such as products moving into Brazil or Russia, or only for high-value shipments. Now, most continuous monitoring data loggers with data transmission available on the market are expensive because they require upfront investment before use. And even then, they fall short in providing 100% ‘real-time’ visibility over these important shipments, including the monitoring of environmental conditions (temperature, humidity, light) and product location.
 
Due to cost concerns, unreliable manual data retrieval, and additional waste attributed to shipments from discarded loggers, all partners in the cold chain, even traditionally well capitalized or funded biopharmaceutical innovators, have resisted pursuing ‘real-time’ data loggers as an option to connect all shipments across the cold chain — resulting in limited visibility only to projects or shipment routes.
 

Predictive analytics powered by big data
The fast-moving, constantly changing cold chain can be impacted at any moment by a variety of environmental factors. The greatest risk to a therapeutic product in transit is the impact of weather to both temperature control and on-time delivery. A late or damaged load has downstream effects that can lead to medicines and vaccines not reaching patients.
 
Today, the biopharmaceutical cold chain requires more than 100 percent visibility. While the focus continues to bear on late and off-schedule loads that threaten to disrupt operations, trigger a compliance event, and eventually challenge their regulatory reputation, a shift towards utilizing predictive analytics to drive proactive management practices would lead to more informed decision making, ensuring efficiency, and productivity across the supply chain.
 
Utilizing a wide range of weather data, a predictive analytics capability allows biomanufacturers and logistics partners required to follow FDA regulations to have instantaneous, ‘real-time’ visibility on 100% of the shipments in their supply chains, without a cumbersome data logger system in the way. Data logger solutions on the market today cannot support this capability due to latency and the inability of their technology to provide up-to-the-minute information.
 
In a recent LogiPharma publication (Differentiated by Supply Chain: A LogiPharma Report 2018), the majority of respondents, including 100 top decision makers in the pharmaceutical industry, cited innovating their distribution channels to remain competitive and improving end-to-end visibility as among their key challenges, and view Big Data — machine learning, artificial intelligence, and advanced analytics — as likely having a considerable impact on the supply chain in the next five years. Traditional data loggers will not get them there.
 

The smart cold chain is here
To offset the challenges that threaten to disrupt cold chains and impact their ability to comply with FDA regulations for pharmaceutical shipments, manufacturers, wholesalers, distributors, and other partners are requiring greater visibility and proactive management. Previous track-and-trace methods powered by next-generation data loggers cannot meet end-to-end supply chain visibility required to move the cold chain from a cost center to a core competitive differentiator. Instead of embracing the status quo, businesses and their logistics partners should consider where they will want to be in five or ten years. Rather than simply looking at data loggers or other hardware, we recommend that companies should actively look to the ‘big data’ and ‘machine learning’ solutions to build a smarter, cost-effective, and resilient cold chain. It’s already possible.
 
Building an efficient and proactive cold chain goes beyond 100% monitoring your assets continuously. It requires the ability to foresee what will go wrong, where it will go wrong, when it will go wrong and what needs to be done to avoid the risk in the first place.
 
To be proactive, the biopharmaceutical cold chain needs to leverage more than the power of ‘real-time’ data loggers and the data they provide — not only to know how their shipment or goods are doing continuously, but also to ascertain what it is that could put them at risk — weather patterns that are the root cause of a potential nonconformance.
 
When considering a cold chain environmental and product movement data visualization and decision support tool, the most important aspect is the data analytics. It is about making sense of data, and making sense of it at an extremely granular level while still demonstrating how to run your cold chain broadly. It involves the analysis of your past cold chain logistics patterns and tying them together with external data streams to inform you of the action to take in any given situation.
 
The right cold chain solution with advanced data analytics enables businesses to streamline operational efficiency, improve visibility and collaboration throughout the supply chain, optimize route performance, and manage by exception. If temperatures within a refrigerated truck shipping pallet traveling through the Valley of the Sun are deviating, knowing that the temperatures are going up is necessary, but it is not sufficient. To project this shipment, you need to get to the root cause. Predictive and prescriptive analytics do just that, but through accurate forecasting they can also prevent the riskier shipment schedule from happening at all.
 
Continuously updated data monitoring and analytics are necessary, but they aren’t enough for a smart cold chain. Effective businesses transporting temperature-controlled products are turning to a new type of partner — a service provider who delivers accurate, immediate information through an established and proven visibility platform, and who can also automate the cold chain data visibility and logistics process. Players in the cold chain transportation business seek service providers who can add significant value and expertise, and that have the following attributes:
 

  • A track record of continually innovating solutions to surpass customer and industry needs.
  • An extensive history with complex biopharmaceutical cold chains.
  • Deep understanding of the complex algorithms necessary to tie ‘big data’ weather events to lane-specific, network-specific temperatures.
  • Thermal modeling expertise to translate thermal profiles into thermal packaging performance
  • A cloud-based platform that is highly-scalable with proven uptime, and is fully-validated.

 

REFERENCES
Dr. Bernhard Schwister et al, “Part 1: Challenges and Opportunities” Page7; “Part 2: Investments and Actions”, Page 15. Differentiated by Supply Chain: A LogiPharma Report 2018 (2018). https://plsadaptive.s3.amazonaws.com/gfiles/_jSIFG4124_wbr_logipharma_report_2018 (accessed March 6, 2019).

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