Abstract 4862: PredICT: a novel solution for capturing, assembling and combining in vivo data
Authors: Rhys DO Jones, Marie Cooke, James Hinchliffe, Justin Morley, Simon T. Barry
Journal: Cancer Research (2015)

Abstract Historically, within large pharmaceutical R&amp;D organisations, the inherent complexity and diversity of preclinical in vivo PKPD and efficacy data has encouraged a culture in which data capture and storage is generally informal and spreadsheet-based. This makes data sets both hard to find and hard to accurately interpret and limits the useful lifetime of the data. Cross-study analysis becomes inherently difficult, time-consuming and error-prone due to the manual effort required to identify, obtain, understand, collate and transform data sets from multiple studies into appropriate formats for visualisation and analysis. With ever more sophisticated studies being generated exploring doses, schedules and combinations, and with the increased reliance on quantitative systems pharmacology approaches to support drug projects, there is considerable need to treat data as the vital asset it is recognised to be, and to secure and exploit it in an effective way. PredICT is a novel platform built to integrate, manage and analyse in vivo data in a much more coherent fashion. The requirements in terms of the data workflow tools of in vivo pharmacologists and modelling scientists were determined which required the development of features that enable effective data organisation and flow. Specifically (1) data capture tools that integrate into the existing scientist workflow enabling efficient and automated data upload; (2) a generalised language and database for storing in vivo studies including the full details of the dosing regime and measured endpoints; (3) a data query tool and interface to quickly and efficiently identify the studies and data of interest in order to collate and present a summarised set of results; (4) an automated export feature to deliver data into formats that can be directly imported into visualisation and modelling software. Complex Oncology in vivo studies assessing multiple dose and schedule groups can be expressed in the database, providing all the primary and meta-data to re-create the experiment numerically. These studies can then be visualised in a query tool that collates data from across multiple studies to enable integrated analysis. PredICT platform has transformed the way in which all project scientists handle in vivo data at AstraZeneca. For in vivo pharmacology scientists the benefits are a more streamlined workflow for the capture and storage of data, along with an effective and efficient tool for identifying and retrieving data from across multiple studies, projects and in vivo models into a single data table. For modelling &amp; simulation scientists, it has reduced the proportion of time spent dealing with data organisation from approximately 50% to less than 5%, allowing them to focus on deriving scientific insight rather than hunting for and manually collating data sets. More broadly, the platform secures in vivo data in a consistent and robust manner, which ensures data integrity for long term use and reference. Citation Format: Rhys DO Jones, Marie Cooke, James Hinchliffe, Justin Morley, Simon T. Barry. PredICT: a novel solution for capturing, assembling and combining in vivo data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4862. doi:10.1158/1538-7445.AM2015-4862