An AI Approach to Generating MIDD Assets Across the Drug Development Continuum.

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making.

Most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modelling efforts that support MIDD and thus yields greater fidelity in decision-making.

Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy.

The paper highlights early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach. Aridhia’s DRE provides a workspace environment with a full suite of tools that can enable an AI approach and also securely connect to other external AI platforms providing external data and compute environments.

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