Reasons External Collaborations Fail In Drug Discovery




This white paper, brought to you by Dassault Systèmes BIOVIA, discusses the different types of external collaborations research organizations engage in, the reasons that many of these partnerships fail to meet expectations, and an informatics solutions to overcome these issues.

Drug discovery is an arduous and costly endeavor that offers no guarantee of success. In an effort to accelerate innovation, streamline the R&D process, satisfy healthcare expectations and improve the rate of return, pharmaceutical companies have come to embrace external collaborations and their outsourcing partners as an essential part of their discovery programs. This trend towards externalization is significant with roughly 90% of companies outsourcing some steps of drug discovery, and with half of all drugs now resulting from such partnerships.

Pharmaceutical companies have come to the realization that certain activities should no longer be part of their core drug development and testing process, what is more, by focusing on these activities, they are losing a competitive advantage in a fierce market. Gone are the days when contract research organizations (CROs) are called upon only in emergencies; now there is greater reliance on CROs to conduct specific aspects of research. Partnerships between pharmaceutical and biotechnology companies are also driving development — as are partnerships with academia. This trend is growing and 75% of respondents to a recent BIOVIA poll stated they are involved in 10 or more collaborations.

The breadth and complexity of these partnerships suggest that a system must be in place to ensure that proprietary data remains secure and that organizations can take full advantage of all data generated. Unfortunately, this is seldom the case with just 11% of 218 surveyed companies deploying a secure, purpose-built solution. Of the remaining 89%, 34% had no solution to speak of, 19.7% relied upon Microsoft software, 10.1% used Dropbox or a similar file sharing method, 2% communicated via email, and 19.7% used other means. Not only are these methods inefficient, they can leave pharma researchers open to security and legal risks — not to mention the wasted time and data resulting from an inefficient collaboration framework. It’s no wonder that so many collaborative partnerships are ultimately unsuccessful.


Many collaborative relationships within drug discovery face fundamental issues such as data synchronization, data integrity, and network security, which result in projects failing to meet expectations or, indeed, requirements. The first hurdle, however, is often one of time, as it can take up to six months to get an external collaboration off the ground – and this does not include the time required to fulfill all legal requirements. The main difficulty can be setting up the infrastructure to support the work being done, as every collaboration has specific requirements. It can take time to develop a standard and effective way of working, and many problems can occur while a framework is being agreed upon. For example, a pharmaceutical company might work with 10 partners who are all uploading chemical, biological and safety data in different formats. The sponsor company must then spend time manually processing vast amounts of data to ensure that it’s cleaned and reformatted. Not only is this a costly activity, but processing data manually is prone to errors.

Research scientists can find themselves devoting two or even three days per week to this process, which is time lost. To avoid this pitfall, it’s important to have an informatics solution in place that automates the process and ensures all data is correctly formatted, but even here there are challenges. The first challenge is one of synchronization as the pharma company’s research scientists will need to know how any data uploaded by an external collaborator compares to the data currently in-house. If an uploaded compound already exists in the in-house database, the existing compound ID can be applied to the new compound, so that scientists will instantly know what it is and how it should be treated. The right software solution could take care of this automatically whenever appropriate, saving the time and effort required by manual data processing.


It’s important to note that collaborations are often unsuccessful because the partner fails to deliver what was requested. They might not run the assay correctly, or they deliver the wrong compound or one that is low in quality. Many of these issues relate to the way industry has traditionally run collaborations. Data and requests are ‘thrown over the fence’ and then in a month or two the collaborator does the same. Only at this point does someone realize that data integrity has been compromised, or the data is not going to advance the development process.

The solution is simple: Treat all external collaborations as though they are internal collaborations. With continuous, rapid communication, problems are identified quickly and the collaboration adjusted accordingly. Of course, communication on this level is not always easy. Take a typical collaboration, for example, where a pharmaceutical company has a project lead, a developer and an executive who needs to be kept informed. That company is then partnered with three external collaborators — a chemistry lead, screening lead and pre-clinical lead. The progress of each compound needs to be accurately communicated among these three partners. This communication can rapidly become complex, convoluted and subject to breakdown.

Sharing data via email, for example, is often ineffective, especially when everyone involved in the project is not part of the same conversation thread, or they may have to search back through email chains for specific information. Here, there are also the issues of different data formats and data access challenges. External collaborations often fail because partners can’t agree on a way of guaranteeing data security. How do you ensure that data is not visible to people outside the project? How do you ensure that those within the project do not have access to data they are not authorized to see? Compounds and company-critical information must be secure, and some data will often need to be ‘walled off’ to specific collaborators. For example, while the pharmaceutical company has access to everything, safeguards may need to be in place to ensure that the chemistry CRO and biology CRO are not able to see each other’s data.


Ensuring the success of an external collaboration requires a suitable and secure informatics platform like BIOVIA ScienceCloud. With this proven, cloud-based platform pharmaceutical and biotech companies can not only securely share data and documents with collaborators; most importantly, they can also share the context. ScienceCloud doesn’t exist in isolation and there will always be a range of hosted data sources and applications that enable the transfer of data back and forth between collaborators as appropriate, while also ensuring that workflows are seamless. Some aspects may be done in the cloud, while others may be conducted in-house. It’s important that the two environments be linked and that data is in the right place so that scientists can step through those workflows quickly—and without any advance knowledge of the applications being used.

A critical advantage of ScienceCloud is that all partners can use the same applications in a collaborative project framework that mirrors internal processes. The applications within the platform are diverse and capable of handling both structured and unstructured data. They facilitate all aspects of data management from data clean-up and analysis to reporting. The BIOVIA Pipeline Pilot scientific workflow authoring application saves both valuable time and expense by automating manual and error-prone tasks such as reformatting data, while also enabling workflows to be shared across project teams. Automated data transfer can be implemented with Pipeline Pilot and a new collection of components called Project Data Collection, which makes it possible to give different collaborators access to specific data. All data is live and data exchange occurs in real time.

One key point that can sometimes be overlooked is the process for closing down a project. There are very important steps that need to be completed in accordance with strict legal requirements concerning the data and what happens to it. For example, there might be a requirement to archive the data, or companies may need to delete it. All aspects of the project lifecycle are formalized within ScienceCloud, and the idea structure always remains the same so that scientists can start uploading and sharing within a relatively short period of time. If the data is fairly straightforward, for instance, uploads can begin as soon as accounts have been created.

ScienceCloud applications include Exchange, which allows scientists to share services and components with others, and a Notebook capability that allows scientists to document experiments, request that compounds be synthesized and deploy tasks. The ability to analyse data where it has been captured in the notebook system of record offers a competitive edge by providing the most complete, accurate and timely data, nearest to the point of operational entry where it can be used to feed other systems. One template can be used by all collaborators to document everything and ensure that no information is missing. A filter is also provided so that any document that references specific chemistry, for example, can easily and quickly be found. If new compounds are synthesised, all IP is accurately and securely captured along with the experiment descriptions and clear ownership rules are applied. ScienceCloud has recently undergone further development in the areas of biologics registration, bio workbench, screening, browse and search, and inventory.


When first beginning an external collaboration project, certain criteria need to be satisfied – a legal framework must be put in place, provision has to be made for accounts and business rules need to be established. Organizations can set up business rules for validation and validation scripts using Pipeline Pilot and Project Data Collection. By implementing these rules and ensuring protocols are in place, every collaborator will register compounds in exactly the same way, ensuring high quality project data.

The security of that data is also of paramount importance, and ScienceCloud provides multilayered physical security such as alternate data center disaster recovery; network security, which includes the implementation of measures such as IP address restrictions; application security such as username/password authentication; and personnel security including who can access the data center. Furthermore, ScienceCloud is the first information management and collaboration environment to be granted ISO 27001 certification, one of the most widely recognized and internationally accepted best practice standards for information security management.

Additionally, BIOVIA recognizes that mobile access can often be critical to the success of external collaborations, given that collaborators can typically work around the clock within various time zones. The majority of the technology offered by ScienceCloud can be accessed on mobile devices, allowing scientists to check on compounds, hold conversations and create tasks, among other things. To collaborate in the cloud effectively, scientists need to have ongoing discussions and “social” interactions with their partners, regardless of their location.

The days of working within isolated information “silos” are far behind us and successful drug discovery now depends on transforming the way we communicate and share data with external collaborators. It’s critically important that whatever informatics solution is chosen, it can address all of the points detailed in this white paper and, most importantly, has appropriate security in place. With the confidence that security provides, partnering organizations can be free to focus solely on optimal scientific collaboration and successful drug discovery.

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