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Image courtesy of Robert Janules. 

According to a December 2009 article published in Nature Reviews1, “the number of new drugs that are approved annually is no greater now than it was 50 years ago.”  

It’s no secret that drug discovery is an arduous process. Much of the “low-hanging fruit” has been picked, and identifying promising leads can take years of research and millions of dollars. Furthermore, steadily rising costs, patent expirations, and increasing regulatory burdens compound the challenges that drug companies face.

As established industry players and start-ups alike seek new opportunities for innovation, biotherapeutics research has been steadily gaining steam, but are organizations simply jumping from the frying pan into the fire?  Biological entities such as proteins, antibodies, vaccines, viruses, or siRNA can be even more complex than small molecules, so it stands to reason that applying the same R&D approaches the pharmaceutical industry has relied on in the past would likely produce disappointing results. Just as drug companies are evolving their ideas about how to develop disease therapies, they also need to re-think their reliance on traditional R&D processes and technologies. Three areas that are particularly ripe for a change are compound management, collaboration, and scientific informatics technology.

Compound management
Many organizations underestimate the importance of compound management—having a dedicated system in place to identify and track compounds during the drug discovery process. Getting this right is critical to research efficiency, safety, IP protection, and more, but it can be an overwhelming challenge when dealing with enormous volumes of complex and globally distributed data. As research operations stretch across geographies and time zones, compound information can easily become lost inside isolated departments or within discipline or format-specific systems, instruments, and databases. Additionally, biological research data is especially tricky to track, spanning thousands and even millions of experimental protocols, proteins, cell lines, and the like, to which inconsistent naming standards are frequently applied. Accessing information on just a single biologic entity—in order to find out what is known about it, what scientists are working with it, and what processes are involved in producing it — can be like finding a needle in a haystack. This is not a recipe for speedy innovation.

In the case of biotherapeutics research, compound management solutions need to go beyond the capabilities of the first-generation chemical registration tools used for small molecules. An approach that takes into account the complexity inherent in the field, and that’s flexible enough to evolve with this still maturing research area is required. Thus, a system for biological compound management must not only uniquely identify biological entities, but also collect and integrate the associated processes, workflow and production steps that were taken to produce the entities, and finally track the relationships between them. The system must allow researchers to capture important data surrounding the compounds they are investigating from day one. When all the information surrounding biologics is systematically collected and made available to the entire enterprise, organizations can prevent potential problems and more easily “connect the dots” that lead to new discoveries.

Global research organizations are finding it increasingly difficult to collaborate in efficient and meaningful ways. This is no surprise considering that discovery project stakeholders are often separated by time zones, as well as by departmental, disciplinary and technical boundaries. There’s a reason why smaller biotechnology start-ups are often considered more innovative than big pharmaceutical players: the free-form interaction and personal communication that can spark new ideas simply happens more often when project participants can meet at the company lunch table or knock on each other’s office door. But as they grow or get acquired, smaller, more nimble firms lose this collaborative edge, and additionally lack the technology required to maintain cross-organizational communication and share knowledge on a larger scale. Large organizations need to harness the power generated by their breadth, while avoiding the stagnation that can result from their size-related inefficiencies.

How can today’s drug companies make collaboration a priority again? Organizations need to be able to re-create the open atmosphere that existed when research team members were in the same building, but on a scale that embraces the breadth and complexity of the global scientific enterprise. This requires several new approaches and technologies. For example, cloud computing provides an ideal forum for project stakeholders to interact regardless of where they are located, or how much data is involved. (Think along the lines of Flickr for R&D.) Social networking technologies allow researchers to contribute information, propose new insights into a problem or share ideas and opinions in an ad-hoc way. Also important is the ability to capture, integrate and manipulate data across the entire research enterprise, rather than rely solely on a hodgepodge of tools that only target specific tasks or disciplinary areas (i.e. chemistry or biology). Better collaboration and more broad-ranging scientific informatics technologies go hand-in-hand.

Scientific informatics technology
Few would disagree that scientific informatics technologies should enable rather than disable innovation. Yet many of today’s drug research organizations are held back by informatics tools that were designed for an earlier era, when globalization, unprecedented complexity, and data overload were not as much of a challenge. As the scope of the scientific enterprise has expanded, information management tools have not kept up, leaving individual departments or groups within organizations to deploy point systems and applications that only apply to their unique needs. The problem is, when it comes time to improve efficiencies across the entire discovery lifecycle—from early research all the way through to clinical trials — the resulting “patchwork quilt” that springs out of attempts to share data or deploy unified scientific processes is an integration and maintenance nightmare. Furthermore, without a holistic view of the organizational knowledge that originates from multiple sources and departments, project stakeholders can easily overlook important insights due to obscured data.  

An end-to-end enterprise foundation for scientific informatics is needed—one that can facilitate the data integration, process automation, and information sharing required for better compound management, for virtualized science like modeling and simulation, for richer collaboration, and a host of other activities that have a direct impact on innovation. Thanks to the advent of solutions that utilize web services and more flexible IT infrastructure, this is now possible. Web services enable processes like analysis or reporting steps to be broken into “parts” that can be put together in different ways, depending on the need of a specific project. Because these parts can function independently from their source system or application, they can be used to create automated workflows and data integration that crosses enterprise boundaries, ultimately streamlining, speeding, and reducing the cost of discovery efforts.

Drug companies can’t afford to repeat the mistakes made in small molecule research when it comes to informatics. “Wait and see” strategies simply delay organization and efficiency. Less time managing, integrating, tracking, and manipulating data frees researchers to devote more time to science. Richer collaboration leads to faster discoveries and fewer research redundancies. Enterprise-level scientific informatics drive the efficiencies and information visibility needed to power smart and successful innovation. And it is only by increasing innovation while simultaneously lowering costs that drug companies can begin to meet the challenges to come in the next 50 years.

1. Munos B, Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov. 2009; 8(12):959.

About the Author
John Conway is currently a global director of Scientific Informatics and Solutions at Accelrys. John’s early career includes serving as an analytical biochemist at Tektagen Inc. (now Charles River Laboratories), a senior forensic scientist at the Pennsylvania State Police and many years at Merck and Co Inc. with varying roles in biological and chemical informatics as well as computational science methods and modeling. Before he joined Accelrys, he was the site head and global chair of the Structural Biology Domain for the Discovery Informatics Department at GlaxoSmithKline. John’s education includes biochemistry and molecular biology at Pennsylvania State University and Lehigh University.