Increasingly stringent safety and efficacy standards, combined with the business challenges of rising development costs and greater frequencies of failure, put pressure on the pharmaceutical industry to improve the quality of drug candidates nominated for development.  An overarching strategy to address these challenges, put forward by the FDA in its Critical Path Initiative, calls for the development of new tools that can address safety and efficacy issues of candidates in faster time frames, with more certainty, and at lower cost.

The initiative calls, primarily, for a transition to a more model-based approach in pre-clinical pharmacology development based on the learn-and-confirm paradigm.  This is often referred to as pharmacometrics.  Secondarily, the initiative calls for establishment of a tight link between measured critical-to-quality attributes and safety and efficacy of the drug candidate in the patient.  Accordingly, preclinical pharmacology becomes a crucial part of the Quality-by-Design solution by providing a “design focus” for specifying drug product performance specifications.

Inference for QbD is used within the pre-clinical pharmacology environment to manage and support pre-clinical studies in the following areas: