Pharma of the Future Historical Timeline

Pharma of the Future Historical Timeline





1997 Collaboration 1.0 Developed software to help scientists organize data, analysis programs, graphics, and text, into one electronic document PERSPECTIVE Hypertext Data Analysis Mapping software released
1999 Real Time PK and PD Data Assembly Services initiated Identified critical information required to deliver timely and accurate analysis-ready datasets for M&S efforts Formal policies and procedures for receiving treatment codes and working with clinical trial data during study execution before database lock
2001 Installed NONMEM Grid Engine Moved from desktop installations to fully automated grid engine for managing computations Savings in time required to manage modeling and simulation activities on the grid
2002 NONMEM moves to the cloud Developed validated web interface for NONMEM to facilitate model development for academic institutions First generation PANDA software released
2003 Pharma of the Future initiative begins Investigated the Factory of the Future program and its implications for Pharma R&D Standardized the M&S workflow
2004 Cognigen becomes a laboratory Implemented research and training initiatives to reduce flow times, improve quality and consistency of work products Definition of systematic, process,  and informatic needs of M&S activities
2005 M&S process formalization Identified the primary tasks, defined the critical inputs, and standardized the outputs Company-wide standards for M&S project execution.
2006 Refined Data Assembly process workflow Defined the tasks, procedures, and interactions for building an analysis-ready dataset  “Guide to NONMEM® Dataset Creation” training manual published for Cognigen data programmers
2006 Knowledge Portals implemented Secure, virtual private networks establish for client communications and data transfers
2007 Data assembly process improvement Implemented continuous process improvement techniques and forensic tools to identify root causes of rework in data assembly Reduce time required to deliver high quality analysis-ready datasets with less errors
2008 Graph Library established Analyzed commonly-used graphs to identify critical elements required to produce graphs more efficiently Provide the right graphs at the right time, with the right information, to tell the story of the data analysis effort
2009 Focus on Integrated Project Teams Investigated strategies to more fully engage interdisciplinary teams in model definition and use of M&S results Use of conceptual schemas as a blueprint for model development
2010 Forensic Pharmacometrics Developed a systematic approach to independent peer review of model efforts and reports Pre-submission reviews of regulatory documentation and reports
2011 PANDA grows up Redeveloped PANDA to provide a model development platform for global teams First enterprise license for KIWI software sold to global pharmaceutical company
2012 Progressive Reporting Strategy Investigated knowledge threads across M&S project execution life-cycle Canonical document library: project justification and resource allocation, analysis plans, and technical reports
2012 Scientific Workflows Refined workflow structure and requirements for common analysis activities Formalized workflow definitions for logistic regression and time to event analyses
2013 Cognigen move to new headquarters Upgraded computer systems, automated off-site backup storage, installed green technology to reduce computer cooling costs Fully validated, secure IT infrastructure and business continuance strategy
2014 The Paradox of Scientific Excellence Investigated enterprise barriers to innovation and the source of silo-thinking Model-based framework for fostering interdisciplinary collaboration
2014 Data Assembly Automation Initiative Created code library and programming templates and developed enhanced data definition documentation Data assembly process formalization and automation modules
2015+ The Pursuit of Excellence in model-driven R&D begins Investigating strategies for using mechanistic models as a basis for accelerating scientific innovations to improve R&D productivity Wait and see…