Case Studies

Rethinking Preclinical Data Collection

"We’ve used PreDICT for over 150 projects and the data capture and analysis has involved the consideration of over three and a half million result data points..." Susan Galbraith, SVP of Oncology IMED from AstraZeneca

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In the world of pharmaceutical R&D, data from clinical trials is managed extremely rigorously – patient safety must be monitored carefully, strict regulatory standards apply, and data protection and privacy laws must be followed. It might be surprising, then, that relatively few standards and tools exist to support the capture and management of preclinical data, despite it often being just as complex and varied as clinical trial data.

Many drug discovery organizations have a culture where capture, storage and sharing of complex data sets is spreadsheet-based, which leads to many practical problems. These data sets are pivotal to the development of every new drug and, compounding the challenges, the studies are generated and modelled by multiple scientific groups, making data and knowledge management and timely communication incredibly difficult.

This is starting to change as more and more pharmaceutical companies are embedding data science, systems biology approaches and predictive modelling into their R&D pipelines in their efforts to improve confidence in candidate drugs and avoid costly late-stage clinical failures. This has sharpened the industry’s appreciation that experimental data is a strategic asset that needs to be actively managed. Preclinical data from later phases is particularly critical as it plays a major role in making decisions about whether and how a drug will be administered to humans in clinical trials.

Developed in Partnership

Developed in partnership with data analytics company Tessella, AstraZeneca’s PreDICT (Preclinical Data Integration and Capture Tool) platform is designed to capture and analyze a wide variety of different types of preclinical dContata. PreDICT ensures data integrity and allows scientists to rapidly find, integrate and share preclinical data sets for the prediction of optimal doses and schedules in clinical trials.

PreDICT is underpinned by scientist-defined data standards designed to reflect the complexity of preclinical experiments. Key features of PreDICT include: flexible study templates that capture in-depth experiment detail at all study stages; automated data processing and integration; a single, central repository and search tool to enable access to the organization’s entire preclinical data set; standardized and automated study analysis in a configurable calculation engine; output of combined data sets from multiple studies into Excel- and Spotfire-compatible formats to enable easy data visualization and advanced analysis.

Benefits

Susan Galbraith, SVP of Oncology IMED from AstraZeneca, adds: “It’s incredibly important that the management of our data is handled in the most accurate and efficient way possible, in order to facilitate understanding and to more accurately predict clinical outcomes. We’ve used PreDICT for over 150 projects and the data capture and analysis has involved the consideration of over three and a half million result data points. PreDICT helps us analyze and visualize data quickly so that we can use it to deliver meaningful clinical benefits.”

AstraZeneca has seen significant benefits from using the platform:

  • Assured data integrity for thousands of preclinical studies.
  • Rapid integration of a wide variety of different types of preclinical data.
  • 30% time saving for modelers producing PK/PD models to understand and predict exposure-response relationships.
  • Reduction of modelling outsourcing costs by up to 20%.
  • Time savings from simplified workflows of 1½ -2 days across every study in bioscience and DMPK.
  • Significant improvements in data quality and confidence in data.