The articles “The Future-as-a-Service” and “Analytical Knowledge Transfer presents a Challenging Landscape in an Externalized World” nicely lay out the informatics challenges associated with dealing with LIVE data (raw) versus DEAD¹ data (processed) when it comes to the externalization of scientific research and development. As their authors point out, there are a multitude of less-than-adequate solutions that can be applied to this problem. One of the biggest contributors to the problem is how each business or portion of a business enters into a relationship with an external CRO partner.

Most, if not all of the time, the business driver is reactive, functional and financial, rather than strategic. For example, a small firm may have valuable IP but not the funding or time to set up their own in-house R&D laboratories, or large more well-established companies may be looking for ways to reduce costs and/or grow earnings. Neither of these situations creates environments in which a holistic approach to managing external R&D relationships are easily supported, nurtured and grown.

Having a well-thought-out strategic sourcing model allows a company to fully integrate internal human and capital resources within the context and framework of effective utilization of external resources. This can lead to improved FTE hiring and capital acquisition decisions. An added benefit is that CRO partners, or potential partners, are more easily able to identify where they may fit into the innovator’s business model, allowing them to better focus their resources and investments to meet the innovator’s R&D goals.

In this article, we describe a model in which R&D organizations develop a sourcing strategy that is aligned with how they add value to their customers and shareholders. Many businesses understand this at a high level, but have failed to translate it to their business operations. Within the scope of this document, we limit our discussion to analytical-based activities, but this model can be applied to all functions within an R&D organization and into manufacturing. There are significant benefits to an entire organization using a single model, as it ensures alignment between strategic priorities across departments. At the end of this article, we will describe how the application of our strategic sourcing model has enabled a mutually beneficial three-way partnership that enables LIVE data to be transferred from the CRO to our own databases within minutes of the data being generated and approved at the CRO.

We define “strategic” as having two categories:

1. work that has a proprietary technological component that the company does not wish to disclose externally and/or
2. work that benefits the business by being done in close proximity to other members of the R&D team (i.e. the speed of process development)

For material generation functions of a company, strategic definition number 1 usually dominates, whereas for analytical functions, strategic definition number 2 usually dominates. Too often, companies decide to outsource work that is considered “routine.” We avoid using this term, as by its very nature it leads people within the organization to consider work that is described as routine as less important and, therefore, less valuable, which by definition limits people’s willingness to invest in a sustainable solution to support it. We suggest that work that is considered less important or less valuable simply should not be done by either the innovator or their partners.

Companies should limit their efforts to work that is both important and critical to the company’s efforts. Within the definition of “important” there are two sub-categories:

1. important and urgent
2. important and not urgent

By our definition, all urgent work is critical to the company’s strategic efforts, as the urgency implies that other functions are waiting for the data to make decisions. As a result, all important and urgent work should be done internally (Figure 1, Quadrant 1), if possible. When this is not possible, doing this work at a CRO but inside an FTE team is the next best option (Figure 1, Quadrant 2). This solution limits the number of people who are exposed to the work product and, further, the priorities of an FTE team can be quickly adjusted in concert with the needs of the material generation functions. In the “Important and Not Urgent” category there are two sub-categories:

1. predictable
2. not predictable

Predictable is defined as worked that is governed by a qualified or validated method and a protocol. The qualified or validated method ensures that the method will produce meaningful results in a reliable manner, and the protocol defines how much work there is and when it needs to be executed or completed. These two attributes make the work product predictable, but not necessarily easy (Figure 1, Quadrant 3). The last category of work we have defined as “niche” (Figure 1, Quadrant 4). In our model, “niche” work is usually very important, but seldom strategic, and is often governed or mandated by industry regulations. Examples of work in this category are extractable and leachable or trace metals analysis. In this category, the innovator is specifically leveraging the technical expertise of CROs. These niche disciplines may require specific skill sets for which an innovator only has occasional need, whereas a CRO can aggregate need from several innovators to support permanent staff.

Once a company has developed good agreement of what are strategic, predictable and niche work activities, a thoughtful approach toward outsourcing can be applied. We have taken a four-quadrant model approach that categorizes work activities while accepting the reality that, most of the time, the 80/20 rule applies, and the lines/interfaces between categories/quadrants can become blurred. Having a well-articulated sourcing model allows a company to agree on a standardized language — agreeing that only important work should be performed whether it is strategic, predictable or niche. This improves predictability for all involved by setting clear expectations for work done internally, and clarifies the role of outsourcing. Defining exactly what work should be externalized also eliminates the need for “approval” for every outsourcing decision. This has the major advantage of enabling a company to accurately and openly articulate its sourcing strategy to CROs, allowing CROs to tailor their customer outreach for what business might be available and to remove focus from what will not be available.

In our model, work activities above the horizontal line can be actively prioritized by the innovator, whereas below-the-horizontal-line work is scheduled and prioritized by the CRO within the context of the associated business agreements. This is why unit work activity below the line is less expensive than similar corresponding work above the line. Predictable, non-strategic work is ideal for outsourcing. In general CROs set up their business to work in the predictable space. This is necessary so they can properly bid on work proposals. As soon as the work becomes too unpredictable, it becomes better-suited for a CRO FTE team, or an in-house FTE effort. However, through the application of this model, companies can significantly influence and change the CRO business through active engagement, partnership, and training of the CRO staff.

Our approach allows for innovators and CROs to explore the natural migration of strategic work from Quadrant 1 that was once strategic, counterclockwise through the FTE team (Quadrant 2) and, ultimately, into the predictable space (Quadrant 3), or clockwise from strategic work (Quadrant 1) into the niche space (Quadrant 4) and again, ultimately, into the predictable space (Quadrant 3). Recently, our industry has observed migrations from the strategic (Quadrant 1) in both clockwise and counterclockwise directions for metals analysis and extractable and leachable analysis. Ultimately, having a well-defined model that drives outsourcing decisions allows for mutually beneficial investments by both the innovator, as well as the CRO.

Even the best strategic sourcing model is significantly hampered by the difficulty innovators have getting access to all the LIVE data that a CRO generates on their behalf. To this end, we have recently embarked on collaboration with key partners to invest in data handling solutions that will enable LIVE data to be transferred from the CRO to our own databases within minutes of the data being generated and approved at the CRO. Enabling the immediate and seamless transfer of the LIVE raw data allows us to further reconsider the current boundaries for what work can be performed at a CRO.

Automated transfer of data from the CRO network provides key advantages, the absence of which can lead to hesitancy towards adapting a sourcing model like the one described in Figure 1. The first advantage is the elimination of innovator FTE time spent delivering numerical results to sample generating groups. This decreases the turnaround time for delivery of results which may be used to make decisions, examine trending, and complete regulatory filings. Although companies may utilize different software to report these numbers, creating a translation and delivery mechanism as part of the automated data delivery process is generally a simple customization. Secondly, the automated transfer enables delivery of sample and instrument metadata. Any recorded information regarding the experiments completed can be categorized and delivered to the innovator’s systems. For example, metadata such as the instrument preventative maintenance status can be accessed by the innovator as if it had been collected in the innovators laboratory.

A third key advantage is providing the innovator access to LIVE raw data. This provides the innovator with several important functionalities. It enables the innovator to dig deeper into the data to answer questions. For example, “have we seen this peak before?”, or in a pre-approval inspection a regulator could ask, “can you show us the raw data?” It also allows the presentation of data collected internally and externally to be consolidated. Overlays for a comparability reports can easily be constructed with data from internal analytical functions and CROs together. The innovator is able to subject CRO data to the same disaster recovery and redundancy standards that it would for internally collected data. Some innovators have engineered solutions to allow a CRO to deliver data remotely into their internal systems. This arrangement achieves similar endpoints; however, it may not be a sustainable solution. Granting access to innovator systems requires a significant investment in a specific CRO, and thus could impinge on a competitive environment to address CRO capacity or performance issues. Further, it requires innovator-specific training, software licenses, and IT support. Those costs are typically the burden of the CRO.

Creating a sustainable, expandable CRO-innovator data automation process requires two major considerations. The first hurdle is to address the wide range of data types deriving from the laboratories of a CRO and an Innovator. Even for analytical data collected internally at most innovators, there is no universal and sustainable solution for viewing and processing current and historical data. For a single discipline of analytical methods, there are likely several instrument and software vendors, each providing a proprietary data format. Extending your network to analytical labs in the CRO network further increases the variety of instrument and software vendors used. These inconsistencies can be overcome by utilizing pioneering software solutions, such as the ACD/Spectrus Platform (Advanced Chemistry Development, Toronto), which can view, process and store data collected on instruments from most leading analytical vendors. Further, the Allotrope Foundation aims to create a universal data format to address this serious business need, already recruiting a significant coalition of industry leaders to drive change in analytical data standards. These types of solutions are able to accommodate the many data formats that may be delivered from the CRO network.

Importantly, the movement of data between CRO and innovator systems must be automated with minimal FTE effort. This can be achieved via several technological means. Solutions are available from vendors, such as BioVia, that physically shepherd the data between locations. One major concern from CROs is the access that each innovator has to their internal systems, which contain data from all of their clients. To address this, an FTP (file transfer protocol) site can be used as a neutral intermediary, allowing the CRO to deliver exactly the files it wants to send (Figure 2). This solution also allows customization of the cadence of data delivery. For example, the data can be uploaded as soon as it is collected, processed or fully reviewed. On the other side of the solution, the innovator can automate the delivery, processing, and archival of the files using a simple periodic sweep of the FTP site. Once the data is housed internally, the delivery of relevant data to internal systems is completed using an automation routine. For CROs, where the data itself is their product, their business process stresses consistency and standardization in data reporting. This makes automated delivery of data, such as numerical endpoints to a LIMS system, a sustainable automation requiring only infrequent lifecycle management. Once file-naming and metadata standards are set for the first participating CRO, the requirements for data sharing and delivery can be built directly into the RFP for the work. By incorporating these considerations into a data-sharing solution, the access to data collected internally or at a CRO are truly on the same playing field. This equality enables innovators to judiciously apply the Analytical Sourcing model described above.

Summary

A robust and well-articulated sourcing strategy coupled with a seamless way to transfer LIVE data between CROs and innovators allows innovators to define CROs in the context of “partners.” This paradigm enables conversations to focus more around what work should be performed internally and what work should be performed at the CRO. This paradigm can enable both the innovator and CRO to efficiently utilize resources to meet their long-term strategic goals.

Reference

1. “Analytical Knowledge Transfer presents a Challenging Landscape in an Externalized World” by Sanji Bhal nicely summarizes: “‘DEAD data’ has the scientifically-rich information stripped away and reduced to some text strings and static images. As a result, ‘DEAD’ data is difficult to search, and impossible to re-process, re-analyze or compare with newly acquired ‘LIVE’ data sets. Unable to interrogate the data, scientists are left at a crippling disadvantage to re-use or make decisions based on it.”

Brian Fahie is Director, Technical Development and Evan Guggenheim is Scientist II, Technical Development, at Biogen. They may be reached at editor@ScientificComputing.com.