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Emerging master data management software promises to improve efficiency of drug development process.
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With drug pipelines looking less and less promising, and a challenging political climate, pharmaceutical companies are scrambling for new ideas to stay profitable during these troubling economic times. Many have turned to cost cutting and productivity improvements in order to stay competitive. While these approaches are suitable short-term responses, they do not address the root cause of the problem, namely the challenge of accurately identifying promising therapies and accelerating the research and clinical trials process to more rapidly bring these therapies to market.
It is common knowledge that drug introduction is costly and time-consuming. The average drug is the result of 12 to 14 years of effort and millions, if not billions, of dollars spent. Yet despite these steep investments, thousands of therapies are discarded for every one successful drug market entry largely because of the numerous and multi-step processes that are required.
In response to this reality, researchers and those responsible for clinical trials management are searching for innovative ways to accelerate these processes and reduce the size of the slope. One new approach involves improving the capture, management, and sharing of data related to compounds, research, and trials. In order to achieve this goal, companies are turning to the emerging software category called master data management (MDM).
The data challenge
A deeper look at each step of the drug development process reveals a recurring theme of missing, incomplete, or erroneous data that wastes time and resources and adds months or even years to the drug introduction process.
For example, one drug manufacturer once lamented, “We’re pretty sure that we’ve found the cure for cancer. We just lost it.” This statement, underscores the difficulty researchers have in managing the immense number of compounds that are tested across the enterprise. One cause of this complexity is that compounds are often housed across multiple business units in separate systems, where they are given differing names and identifiers. As a result, successes achieved in one therapy are often invisible to other parts of the business. Worse, side effects and setbacks are also not always shared, meaning the same failed compound can re-enter the drug pipeline multiple times across different therapies, thereby wasting precious time and resources on research that would not have been conducted had full institutional knowledge of this compound been properly shared and managed across the enterprise.
It is well known that not all sites perform in a similar fashion and that some variance can exist for specific sites based on the particular demographics that are being recruited or the investigators involved in the study. Yet most institutions do not have access to the data that would enable them to clearly identify and recommend those sites that would be best suited to conduct the trial based on the compound that is being tested. Better insight into sites and their performance across a number of important metrics could dramatically reduce the time and cost of clinical trials, while maximizing the reliability of the trial itself.
In the same way that all sites do not perform equally, not all investigators can be relied upon. The ability to properly manage the list of investigators and describe their performance against key metrics could also allow clinicians to drive down the time and cost typically associated with clinical trials.
When an organization identifies a lack of promising new drugs for a therapeutic area, many routinely turn to academia for new promising compounds. Scouts seek out these key researchers and establish relationships in an attempt to leverage whatever successes might result from their early-stage research. However, most companies lack clean, reliable, centralized data that can identify these key resources, discoveries and compound inventories or they lack the ability to access it.
The reality is that, in addition to understanding each of these data elements independently, understanding the relationships among them is just as critical. By institutionalizing processes that depict these relationships and by enabling easy access to the data itself, organizations can radically reduce the time of each phase of drug development. Organizations can also benefit from internally generated data such as employees, clinical protocols, and clinical trials in addition to the third-party data that is mentioned above.
Discovering master data management
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MDM is a growing trend among pharmaceutical institutions looking to accelerate drug discovery. In short, master data management aims to manage, integrate, and reconcile disparate data across the various information technology landscapes. MDM requires a combination of process and organization to ensure success, but pivots on important technology enablers. In evaluating a solution for the types of challenges outlined above, several key components of an MDM technology are critical and should be included:
- A flexible data model. The solution must accommodate the different types and forms of master data such as people, compounds, clinical trials, and locations, in order to fully address the different data needs across the drug discovery process.
- Data cleansing, standardization, and enrichment. Given the state of data that is typically found across an enterprise, it is critical that an organization leverage a solution that can continually integrate and cleanse data as opposed to relying on a one-time only data clean-up effort.
- Robust matching and survivorship rules. Establishes a “best version of truth” or “golden record” of data and resolves inevitable data discrepancies in a rules-based manner, which in turn dramatically reduces the need for manual effort.
- Flexible relationship management. Relationships among clinical trials, clinical protocols, compounds, sites and investigators are just as important as the individual items themselves. It is quite possible, for instance, that an investigator performs quite well in one site, but poorly in another. It is also important to understand the relationships between compounds and trials for compliance and covigilence. To be effective, the proper solution must allow for these complex relationships to be maintained.
- Robust data stewardship capabilities. MDM by its very nature will dramatically reduce the work effort associated with data management. However, an ideal solution also allows humans to interact with the data as needed and perform manual data management and exception handling to resolve conflicts and ensure maximum reliability and performance. This is something MDM experts and influencers refer to as “data stewardship.” It is also related to data governance, which is a process that applies a definition and the enforcement of rules and procedures as part of data maintenance.
- Flexible integration framework. While the ability to create reliable data is important, the ability to access and retrieve these data, both through a Business Intelligence solution or through operational systems is just as critical. The ideal solution must allow for flexible integration in batch and real time with the business applications, where research and research-related management decisions are made.
Ensuring a successful MDM outcome
Given the challenges of a weakening pipeline and tough economic conditions, innovative companies are looking for new ways to accelerate the drug development process and improve the bottom line. Early adopters are already starting to see improved performance in R&D and clinical trials management, shaving weeks and even months off of the difficult drug introductory process. The best way to ensure a strong outcome is to create and maintain reliable data that is capable of supporting the company’s current and future business requirements. Achieving a healthy MDM journey will reap many rewards provided that considerations are identified and research is performed right from the start.
About the Author
Joe DosSantos is senior director, Field Solutions for Siperian, Inc. He has consulted with numerous pharmaceutical and life science companies over a 15-year consulting career, helping them to optimize processes and introduce new technologies to improve business performance.
Filed Under: Drug Discovery
