Every drug candidate undergoes efficacy and preliminary safety tests in animals, which constitute the critical bottleneck in the pivotal decision process of whether to pursue full-scale preclinical development. Remarkably, almost all of these animal studies are still performed by hand in much the same way as they were 20 years ago. Researchers manually record data into paper scientific notebooks and electronic spreadsheets and manipulate the data extensively to attain meaningful results.
These manual methods are inefficient, subject to data integrity and accuracy problems, and impede or prevent altogether access to study data throughout an organization. With manual methods, much of the important study or project information, or tribal knowledge resides solely in the heads of individual researchers and is depleted by employee attrition over time.
The inefficiencies inherent to a single screening study are multiplied with each additional study conducted. This adds up when one considers the fact that for every successful drug target discovered and developed, there are 5,000 that fail. Together, these problems constitute a serious but avoidable problem for research organizations: that of prolonged discovery time for new drugs.
Data Collection & Analysis
Data collection for animal studies in many academic and industrial labs commonly involves a technician writing on a piece paper. Sometimes it takes two technicians: one to measure and a second to record results on paper. The data is then entered into spreadsheets where they are manipulated and reformatted for transfer into statistical software to generate meaningful graphs and analyses. This manipulation increases the likelihood of recording, legibility, transposition, and transcriptional errors.
Investigators or technicians must then review the data again in the attempt to catch any errors that may have resulted from the data manipulation. Since most research studies are not subject to a thorough quality assurance review, undetected errors can be compounded, potentially compromising the integrity of study conclusions.
Problems with data integrity and security due to accidental destruction, notebook and datasheet loss, file spreadsheet deletion or change, theft and intentional falsification have also been identified by numerous institutions as serious problems associated with manual data collection methods.
Animal study management software streamlines the data collection process by electronically capturing study data directly from measurement devices and managing it centrally. Data manipulation is minimized because the software automatically generates graphs and analyses that have been formatted to organizational standards, saving substantial time and providing a consistent output format for review.
Research managers can view results immediately, instead of waiting for the study data to be collated, and can share progress with management from other departments. This makes errors easier to spot, and facilitates next-step planning and decision making across the research enterprise.
Study Data Accessibility and Tribal Knowledge
Even within the same institution, investigators rarely follow a consistent data collection and formatting convention within their spreadsheets, making the analysis and comparison of data between studies quite challenging and time consuming. The spreadsheets typically contain measurement data, while the study-specific information (such as disease induction method, animal origin, and strain and drug information), if collected, reside in other files or documents.
Data and results from completed studies are pasted into the research notebook, which is later microfiched and archived. The results may subsequently be re-entered into an institutional database, if one exists.
One problem with the use of paper notebooks is that many details of the materials, methods, conditions, and intricacies of the study are not recorded, and thus not available for later reconstruction of the study or troubleshooting. Much of this critical information about results and study methods typically resides in the heads of the researchers themselves and constitutes the tribal knowledge.
Animal study management software provides researchers and management with immediate access to study information and results. All study information is centrally located and formatted, enabling researchers to search for particular information and drill down into all current and previously conducted studies, and to quickly analyze and compare results between and across studies.
The timely performance of study-specific tasks, such as dosing or sample collection, as specified in the study protocol, directly impact the study integrity. Accurate documentation of task performance is a critical part of ensuring the quality of the research.
Centralized management of planned study events for the variety of studies and researchers is difficult using manual methods. Study task schedules are typically penciled into calendars, which require frequent modification due to their inherently variable nature. When supervisors coordinate the conduct of tasks for multiple studies and technicians, verification that tasks were performed as scheduled is complicated, as is the reassignment of duties when technicians have unplanned absences.
Automated systems give researchers and supervisors greater control over their research processes. By facilitating centralized documentation of scheduled, unscheduled, and completed study tasks using study management software, research management can oversee task duration and completion, perform duty reassignment for unplanned staff absences and shift scheduled tasks as necessary.
Commercially available systems can manage cancer studies and a variety of other animal models and automate study processes such as study design, data collection, task management, data analysis, graphing, and report generation, as well as enable enterprise-wide access to study information for current and archived studies.
The advent of study automation technology provides the research enterprise with a new means to decrease the time to discovery of novel therapies.