Challenges in the Transition to Model-Based Development
Grasela, Thaddeus(1), Jill Fiedler-Kelly(1), Cynthia Walawander(1), Charles W. Dement(2)
(1)Cognigen Corporation, (2)University at Buffalo, The State University at New York
The requirement to demonstrate the safety and effectiveness of new medicines coupled with the critical business challenge of dealing with rising development costs and an increased risk of late-stage failure is creating a ‘perfect storm’ in the pharmaceutical industry.
This presentation will focus on two theses. The first is that modeling and simulation will play an increasingly important role in drug development and that this role will shift over time from a supportive function in the current empiric-based development paradigm to a central role in a fully model-based development paradigm. The second thesis is that the design, deployment and maintenance of a reliable and efficient process for pharmacometric analysis represents a critical challenge the must be addressed if the full value of modeling and simulation is to be realized.
Practitioners of the art and science of pharmacometrics are well aware of the effort required to successfully complete modeling and simulation activities for drug development programs. This is particularly true because of the current, ad hoc implementation wherein modeling and simulation activities are piggybacked onto traditional development programs. Challenges with timely data availability, high data discard rates, delays in completing modeling and simulation activities, and resistance of development teams to the use of modeling and simulation in decision-making are all symptoms of an immature process capability.
A process that will fulfill the promise of model-based development will require the development and deployment of three critical elements. The first is the infrastructure – the data definitions and assembly processes that will allow efficient pooling of data across trials and development programs. The second is the process itself – developing guidelines for deciding when and where modeling and simulation should be applied and criteria for assessing performance and impact. The third element concerns the organization and culture – the establishment of truly integrated, multi-disciplinary and multi-organizational development teams trained in the use of modeling and simulation in decision-making. Creating these capabilities, infrastructure and incentivizations are critical to realizing the full value of modeling and simulation in drug development.