How New Software Aids Data Management and Improves Workflow

How New Software Aids Data Analytics and Improves Workflow

Robust data management and data analysis tools can help minimize failure rates in pharmaceutical development and discovery. The investigation and discovery of therapeutic molecules, proteins, and genes depend on efficiently aggregating and accurately reviewing large collections of data from multiple sources—both internal and external. Thus, the enormity of the data sets associated with drug candidacy and development requires effective tools to store and analyze information. The scope of the data also demands an intelligent approach to information processing.

Problems with Excel and Legacy Data Systems      
Smaller organizations within the pharmaceutical industry generally lack the time and resources for proper data system configuration and ontology. Many startup companies rely on traditional options, like Excel, but they quickly find that such tools are inadequate for the modern data load.

The goal of data analysis is to surface all of the critical information a pharma organization needs to decide which drug to advance through development. Modern software must manipulate these large data sets into readable formats and present the data using visualizations to reveal the most important insights for your organization’s objective.

In addition, systems used to manage drug development and discovery data must be able to construct and manipulate frequently used data formats, such as structure-activity relationship (SAR) tables. Indispensable to pharmaceutical analysis, SAR tables are used to analyze the relationships between the properties and chemical structures of compounds.

Several concerns associated with the use of older systems like Excel include:

  • Poor automation. Excel-based data management frequently relies on manual data entry. Not only do manual entry and manipulation consume large numbers of man-hours, but the process also inevitably invites human error.
  • Limited scalability. Excel and similar tools are unable to process large quantities of data efficiently or accurately.
  • Incompatibility. Excel-based platforms may not integrate easily with other tools and systems, making it harder for different departments and external entities to share data.
  • Poor security. Aggregated drug discovery information is often the most valuable IP a small company owns. Excel lacks robust security features to effectively guard against cyber-attacks and data breaches.

In addition to the lack of functionality, using inadequate data systems is also costly:

  • Poor decisions. Decision-making processes suffer from data management inefficiencies and inaccuracies.
  • Lost opportunities. In the pharmaceutical industry, reduced decision support often undermines competitive advantage.
  • Security breaches. Cybercriminals can hijack or share sensitive information, resulting in financial losses.
  • Expensive system maintenance. Maintenance and upgrades of unlinked tools can be costly and time-consuming.

FAIR Data Principles    
FAIR data principles are based on four characteristics of digital data: findability, accessibility, interoperability, and reusability. When FAIR principles are applied to drug discovery and development data, time and money are saved.

Recent studies in Europe underscore the costs of not applying FAIR principles to health research management. A 2018 report from the European Commission on the expense of not using FAIR data principles estimated a direct loss of about €10.2 billion per year in the European Union. The Commission also estimated an additional loss of €16 billion associated with lost data management innovation opportunities. Thus, the approximate total annual cost of failing to apply FAIR data principles would be €26.2 billion per year in the European Union. Moreover, another report released this year from the European Commission indicated that a monthly saving of 56.57% of the allocated time and €116,800 could be achieved in the EU by using FAIR data principles.

Contemporary data management systems offer scalable storage that can incorporate diverse data types beyond just text and numbers. In addition, processing speeds for intense computations have increased and new algorithms are making deep learning concepts possible, facilitating predictive analytics.

Signals Inventa    
As a solution to the pharmaceutical need for robust data management and data analysis tools, Revvity Signals offers Signals Inventa, a unified platform with greater accuracy, efficiency, and security. Here are a few of Signals Inventa's advantages:

  • Scalability. Signals Inventa is designed to accommodate large, complex data sets, making processing and data analysis quick and accurate.
  • Real-time data. Offering insights from real-time data, it fosters timely, informed decision-making.
  • Automation. With this software, you can automate many data management and decision-support processes, minimizing the need for error-prone manual manipulation and data entry.
  • Security. Advanced security features, including data encryption, multi-factor authentication, and access controls, protect the Signals Inventa system against cyberattacks and data leaks.
  • Customization. The software can be customized to ensure the system is in step with your organization’s unique workflows and processes.

Before loading data into your scientific data repository, carefully consider its categorization, normalization, and annotation for subsequent location, access, and integration. The technologies you choose should be robust and plastic—able to evolve with the needs and objectives of your company.

Dr. Daniel C. Weaver
Director Product Portfolio

Dr. Daniel C. Weaver is the Director and Solutions Architect for Lead Discovery Solutions at Revvity Signals. Prior to joining Revvity, Dr. Weaver was the Director of Scientific Computing at Array Biopharma, Inc. in Boulder, Colorado, where he led all aspects of scientific software development and acquisition. Over the course of the last decade, Dr. Weaver’s team delivered systems to support scientific endeavors ranging from target identification though drug discovery and into clinical development and translational medicine. In a previous life, Dr. Weaver was the Lead Scientist for Gene Expression Analysis at Genomica. He received his doctorate in developmental genetics from the University of Colorado, Boulder under the direction of Dr. William B. Wood where he studied patterning in early development.