The workflow illustration below provides a context for how DMX could be used on a development project and for the potential collaboration between modeling experts, clinical development project team customers, and senior decision-makers.
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The modeling expert works with the project team to develop a modeling strategy on the basis of available information and an understanding of key development questions. In collaboration with the team, the modeling expert builds drug-disease models and performs associated simulations of dose-response for different endpoints, different treatment regimens, different subject populations, and different competing products.
The modeling expert populates DMX with a database that contains simulated probability distributions of efficacy, safety, or other endpoint measures as a function of specific model inputs such as treatment options (drug, dose, dose frequency, etc.), subject populations, and assumptions. The database and its associated definitions, which are created by the modeling expert with the team as part of the modeling process, comprise the range of scenarios that can be explored by DMX. The modeling expert uses DMX publishing tools to upload the data and supporting model documentation, along with any specified dose-response views and team product profiles, to the server for team exploration.
Project team members use DMX to actively explore drug product attributes and associated uncertainty. Because model building and decision-making are interactive processes, new questions will arise, assumptions will change, new data will become available, or certain questions will become obsolete. As a result, models are updated and/or simulated results are efficiently re-published to the server by the modeling expert for ongoing team review and consideration.
Project teams use certain DMX views and profiles that capture the main insights from their exploration of the model to support key development program recommendations.
Senior decision-makers are presented with views and profiles that summarize the rationale underlying team recommendations. They also have the option to modify certain choices themselves, using the summary views and profiles as a starting point.