History of Pharma of the Future
History of Pharma of the Future
Pharmacometric modeling and simulation has moved from its infancy as a novel way of approaching the analysis of clinical pharmacokinetic data to become an invaluable tool in pharmaceutical and biotechnology research and development. Early, tentative applications of modeling have given way to robust model-based drug development activities in which pharmacokinetic and pharmacodynamic modeling and simulation results are widely utilized to improve the probability of successful clinical trials.
Pharma of the Future Historical Time Line
The transition from novelty to mainstay has required considerable effort to overcome the logistical and scientific challenges of meeting demands for the timely delivery of modeling and simulation (M&S) results at program milestones. As we have worked to meet these demands, we recognized that the various logistical and scientific challenges arise because of the unmet, and often unrecognized, changes to traditional processes that are required for model-based drug development (MBDD).
Cognigen’s Pharma of the Future (PoF) initiative began in 2003 as an effort to design and implement the workflows and computer systems required to support M&S services. The PoF initiative is based, in part, on the Factory of the Future (FoF) program sponsored by the United States Air Force during the late 1970’s and early 1980’s. This program successfully reduced the costs and improved the productivity of the aerospace industry, and gave rise to computer-aided design (CAD) and computer-aided manufacturing (CAM) tools and a variety of other software tools and management techniques still in use today.
As we learned about the FoF program it became apparent that the productivity challenges of the aerospace industry were strikingly similar to those now faced by pharmaceutical research & development (R&D). Further, many of the approaches developed to address the systematic, process, and technical needs of that industry are, in fact, applicable to the biomedical and life-sciences industries.
Consequently, the many different M&S projects performed by Cognigen scientists became a source of case studies and research material regarding the challenges of delivering M&S results. We conducted numerous research initiatives to identify and address deficiencies in data definitions and data programming requirements to deliver timely analysis-ready datasets. We designed and implemented a robust informatic infrastructure and computer system to support our computational needs. Lastly, we implemented a variety of internally and externally focused training programs to advance the science and technics required for planning, executing and reporting M&S activities and results.
Since the beginning of the PoF program, Cognigen has continued to refine and formalize its processes, establish a well-recognized quality management system, and implement a state-of-the-art computer system and IT infrastructure to support pharmacometric M&S activities. This has enabled Cognigen to consistently win praise from our stakeholders. Our clients appreciate the quality and timeliness of our results. Auditors have recognized the effectiveness of our quality management system. Regulators have acknowledged the comprehensiveness of our reports.
As our internal processes matured and we realized efficiencies in the execution of M&S projects, we shifted our focus to our interactions with our clients – the scientists and managers on drug development teams. These teams are increasingly multidisciplinary, and while the scientists may be unfamiliar with pharmacometric tools and technics used in M&S, they have a deep understanding of the issues that must be addressed to deliver a successful development program.
To capitalize on this knowledge, we emphasize the use of a conceptual schema – a picture representing what is known about disease biology and drug pharmacology – in our discussions with teams. These pictures enable us to establish interdisciplinary collaborations that identify the knowledge gaps that can result in incomplete and poorly predictive models. This process of collaboration ensures that team members are vested in the accuracy, and success, of the modeling effort. Our approach to collaboration improves the team’s understanding of the model, enables a sense of ownership, and builds confidence in the results.
As we look to the future, we can see that model-driven research and development will be the next step in the evolution of MBDD. In model-driven R&D, interdisciplinary collaborations will lead to more predictive disease-drug models. The models will begin to emerge early in the R&D lifecycle and incorporate our understanding of the mechanisms of disease and the effects of drug therapy as new information becomes available.
Importantly, we anticipate that the models will be used to drive the experimental methods and study designs used to generate critical data. The models will also be used to prioritize the investment in innovative technologies and identify the requirements for new, as yet, unrecognized innovations. In short, model-driven R&D will bring entirely new challenges in designing and governing the R&D enterprise in order to synchronize multidisciplinary activities and bring new medicines to the marketplace.
The PoF program continues to evolve and tackle more sophisticated challenges in model-driven R&D. Our current efforts are focused on knowledge management, informatic standards, interdisciplinary communications, and the synchronization of technology with R&D needs. Cognigen is uniquely positioned to address these challenges and we are committed to exploring and testing new ways of thinking and working that will bring needed improvements to R&D productivity.