Certara offers predictive science and informatics solutions that enable cross-disciplinary and translational approaches to drug development.
Access to preclinical data such as pharmacodynamic (PD), pharmacokinetic (PK), and safety studies is limited by two key factors: i) data is usually stored in different systems, and ii) applications used to capture and store scientific data have only basic, fixed data query mechanisms. D360 has been deployed on both safety and PK/PD data sources to support a variety of workflows from standard study reports, through cross-study analyses, to the generation of more holistic data views where safety and PK/PD can be considered in tandem.
Accessing data from preclinical studies is a standard informatics workflow for pharmacologists, toxicologists and pathologists. Data access from the applications employed to capture preclinical data is often limited to one study at a time and provides data in a format ill-suited for further analysis. With D360, users can set up a single study or cross-study data view in a matter of minutes. Such data views can i) include a variety of data visualizations, making data more immediately digestible and ii) can be created in D360 even where source data may reside in multiple underlying data systems such as Provantis, Pristima, TDS/PDS, or Watson. D360 can be used to examine preclinical data at a variety of levels from individual animals to inter-study comparisons supporting a wide range of users from individual “ologists” to operational and research managers. Any study data view can be deployed to the team in a matter of seconds, making the data view available as a one-click operation.
D360 dashboard widgets provide one click access to study (and other) datasets. In this example the widget leads to a study group dataset which shows those control study groups with a user specified adverse response. The data is presented in a spreadsheet view with automated color coding and connected graphs that show the prevalence of the adverse response within the different study groups and also the distribution of studies retrieved. Such data views assist in understanding prevalence of specific outcomes within and across studies.
D360 offers a variety of perspectives on preclinical data from the level of whole studies, study groups, individual animals, and many others. Understanding the quantitative and qualitative findings from within a preclinical study comprises a key current workflow. D360 allows study data to be presented at the study, study group, animal, or individual result or finding level, and allows the user to explore to whatever depth is appropriate given the data. More importantly, D360 can generate cross-study datasets at these levels, supporting a very wide variety of workflows for a population of users from managers to scientists:
D360’s chemically/biologically aware spreadsheet will be familiar to Excel® users in terms of its range of functionality, with the added benefit that D360 handles indeterminate numeric data (frequently seen in biological results) in data summarization, sorting, filtering, and graphing operations. Drilldown into summarized data values shows individually determined results and related information such as data from individual animals in a study. All D360 data presentations are linked so selection in one selects and auto-navigates in other data views.
Form data viewers are often preferred by scientists for particular tasks and allow richer data presentation, including multi-level datasets, through the use of tables and sub-forms. Building a new form is similar to creating a PowerPoint® slide and can be accomplished simply by any user. Like all other D360 data viewers, forms are interactive with all other dataset viewers.
D360 provides a rich array of standard data visualizations including scatterplots, histograms, grid views, and data correlation matrices to understand correlations. Understanding of correlated effects is enabled by data mapping and the ability to combine measurements and clinical observations through functional equations. D360 also provides strong support for the visualization and analysis of chemical structures (see the Discovery page for further details).
The essence of preclinical data analysis is the determination of statistically significant outcomes and correlations. D360 provides embedded tools for statistical summarization of data and determination of deviation from characteristic or expected values. D360’s functional equation columns can be employed to develop data statistics to determine deviations from mean values, standard deviations, and confidence intervals; and highlight data that may be considered outlying. To support expert statisticians, D360 integrates with commonly used advanced statistics tools such as SAS for Excel and JMP, combining D360’s ease of data access and data visualization with familiar user interfaces.
Learn how to easily access your data
White Paper: Speeding the Iterative Drug Design Process [PDF]
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Webcast: Speeding the Iterative Drug Design Process with D360 [WebEx]
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Download D360 Translational Science Data Sheet [PDF]
