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International Clinical Trials

Avoiding Data Overload

The term ‘cold chain’ generally refers to the transportation and/or storage of temperature-sensitive materials, from active pharmaceutical ingredient (API) to final product. By definition, monitoring of temperature and other physical measures, such as humidity, is therefore always an integral part.

Although an increasing number of experts and industry guidelines define the owner’s responsibility with regard to product integrity over the entire end-to-end supply chain, many companies only focus on certain parts of the chain – for example, up to the point where responsibility is handed-over to the wholesaler. Additionally, the various phases in the development cycle of a product (R&D, clinical trials and commercial) are considered separately. Nevertheless, even though the focus is on just a few steps in one phase within the chain, it remains a complex issue, and data management is a considerable challenge.

It is important to consider difficulties such as: how to collect the data from several countries; where to store the data and how to give access to the right people; and how to allocate additional information to the cold chain data.


Data management encompasses the collection, availability and use of (temperature) data, and the integrity of products during transportation and storage. When using the terms ‘data’ or ‘cold chain data’, it is vital to define the various elements.

When using this terminology, two kinds of data management can be performed: finding reports (based on additional information); and analysis of a single or multiple ‘curves’. A typical search example would be to find all winter shipments from Zurich to Moscow which had an excursion of less than 0°C; whereas a typical analysis example would be overlay all LMV (graphs) from the example search, calculate an average and add marker points accordingly.


In the past 15 years, millions of shipments have been performed and the products were released without the need of data management. So why should it suddenly become important to collect temperature information and manage data? There are three key reasons:

Are You in Control?
Data management is designed to safeguard the integrity of the product. Cold chain data systematically collected and managed helps to measure the effectiveness of the chosen packaging materials, processes and service providers, for example:
  • Is your packaging qualification as effective in the real world as they have been when tested?
  • Are there any changes to your distribution environment?
  • What is accountability of service providers?
  • Are there any opportunities for cost savings?
An increasing number of guidance documents define data management as a regulatory requirement – for example the PDA Technical Report 39, which stipulates that the shipper must collect and archive GMP relevant data for 10 to 20 years in a safe place and in a suitable format (1).

Data management can speed-up the release process. When collecting cold chain data and making them available to organisational units, the release process can be shortened by improving their availability, so that reports are available in electronic format ready to be archived (PDA/A for archiving), as well as making use of automation, whereby ALARM reports are automatically forwarded to quality assurance (QA).

As a result, pharmaceutical companies are increasingly expressing the wish to systematically collect and use cold chain data. Successful projects are motivated by improvement of quality, which safeguards integrity, and/or process improvements, which speeds up the release process. Before starting a data management project, one should define why exactly you want to collect cold chain data (for example, to monitor effectiveness of a chosen packaging method), and what kind of analysis you want to perform (for example, to find reports per trade-lane and count the number of excursions).

Thus to summarise, before starting with cold chain data management, it is important to agree on a rationale for the project, and define your expectations for the analysis it will produce.


A range of challenges await any company trying to collect and manage cold chain data systematically.

Data Collection
When collecting data from shipments to and from worldwide destinations and storage facilities, a number of factors should be considered. Consistency is key: one must make sure that all the data available is collected. Speed of access to the data is also important. System and equipment requirements should be kept in mind, especially if extra equipment is needed at the destination in order to return data. Finally, ease of use is another important factor: is there a need to return, monitor and manually upload or is it possible to perform an on-site assessment?

Release Process
Before the release of a product, a decision must be taken based on the collected data, taking other variables into account. For example, is the right information available, and what are the assessment criteria at the destination? Data security is important too; the data should not have been manipulated. Likewise, one should look in to whether there are any tools that can automate the process.

The integrity of the data is vital. Can it be guaranteed that it is impossible to manipulate the data? Is the access to the data limited to the right people/organisational units? And, is a document control process installed to avoid duplicates and redundancies?

Finding Data
Once temperature records are collected in a database, the challenge becomes how to find them. A search can only be conducted within pre-assigned fields, and furthermore, the search only delivers results if the pre-assigned fields are filled with information.

Feed the System with Additional Information
Reading information available on a datalogger is simple – but how does the additional information get into the system? Does the analyst need to enter information from another system? Centralised control is important; with manual entry there is no control over consistency and opportunity for errors creeps in. Automating information hand-over can help, but system integration can be expensive and difficult to implement.

Another important point to consider is that a database can only be fed automatically if a ‘key’ is available to the raw data of the logger files. In our eyes it is not acceptable that a separate database must be maintained for each manufacturer/logger type. But unfortunately, most logger providers do not have an ‘open source policy’ and only offer proprietary systems.

Therefore, it is important to work with a provider that offers a validated solution. This means that additional information can be fed from another system (such as ERP), with an open source policy that allows the use of one database for various dataloggers.


When looking at the cost of data management in the cold chain, it is essential to take a total cost of ownership (TCO) perspective of the entire monitoring solution. It is impossible to separate the cost of monitoring from the cost of data management. Additionally a TCO-view only makes sense when comparing two different options (for example, TCO monitoring today versus the implementation of a new system). In the following analysis we focus on an example TCO analysis for cold chain data management of a proprietary database versus open DMS.

The cost and benefit is driven by requirements (for example, what functionalities and processes need to be covered to achieve your goals?) and the degree of automation (for instance, which processes should be automated and which should be carried out manually?). For simplicity, we look at cost per year and consider all investment and equipment that will depreciate over a 12-month period. This is in fact a realistic assumption considering the speed requirement changes as well as the industry’s regulatory habit of limiting equipment calibration/validation to one year. But even more sensitive are the assumptions surrounding the process cost, which could also be described as the time and effort needed to perform a single process step. By definition, a cost comparison is only an approximation of reality and is driven by the assumptions – but it is helpful to understand the cost drivers and their dependencies (see Table 3).

It is clear, therefore, that organisations should only chose a proprietary solution in a clearly defined and limited set-up. Opt for an open solution for any other situation, changing origins and destinations, different logger types, and so on.


Data management for the pharmaceutical cold chain is a simple concept on the surface. Those who are responsible for managing this important data know that there are many things to consider. It starts with understanding why you are collecting data to begin with and then understanding the best methods for both collection and treatment of that data. Creating a plan of how to manage cold chain data, and to who the responsibility falls, requires an understanding of your goals, challenges and available options. The advent of PDF data loggers has simplified and improved the monitoring process dramatically: they can be configured at the last minute with productspecific profiles (including multi-levelalarm criteria). At the point of destination, they are unpacked and connected to a PC via a standard USB port. The data logger automatically produces a PDF report containing OK/ALARM, alarm statistics and a graph – without any equipment or software needed at destination. As such, the independent PDF data logger is the new generation of cold chain monitoring.

  1. Assessment of routes and modes of transport is recommended, 61 No S-2: p15, 2007

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Martin Peter holds a Master of Business Economics from the University of St Gallen, Switzerland. As a Senior Consultant and later as Engagement Manager, Martin has worked in the areas of strategy, process consulting/IT, M&A and cost cutting for a business consultancy focused on travel and transport industry. In 2003 Martin joined Envirotainer as Commercial Manager, and had roles in product development, business development, marketing and sales. As Vice President Sales, he was also member of the Executive Management Team of Envirotainer. In this position he was one of the key drivers behind Envirotainer’s transition to become a partner to the pharmaceutical clients and the logistics industry focusing on cold chain management. In 2008 Martin joined ELPROBUCHS, Switzerland, as Director of ELPRO’s cold chain activities. Email:
Martin Peter
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