Model interpretability is not uniformly critical across all stages of model development; however, it becomes essential at key decision points where scientific understanding must support regulatory confidence. In these contexts, interpretability ensures that model behavior is not only statistically sound but also biologically and clinically credible. Interpretability is particularly important in the following scenarios:

During model structure selection, to ensure biological and clinical plausibility

Interpretability helps distinguish between models that merely fit the data and those that meaningfully represent underlying physiological or pharmacological processes. A structurally interpretable model allows reviewers to understand why the model works, not just that it does work, reducing the risk of overfitting or spurious relationships.

When evaluating covariates, to distinguish meaningful relationships from statistical artifacts

Covariate inclusion should be driven by biological plausibility and clinical relevance, rather than statistical significance alone. Interpretability enables assessment of whether observed relationships are consistent with known mechanisms or are likely artifacts of the dataset. This is particularly important when covariates influence dosing recommendations or subgroup labeling.

During simulation and extrapolation to new populations or dosing regimens

When models are used beyond the observed data, such as in pediatric extrapolation, special populations, or alternative dosing strategies, interpretability becomes critical for assessing credibility. Mechanistic understanding supports confidence that the model will behave appropriately under new conditions and that predictions are not driven by unsupported assumptions.

When communicating results to regulators to ensure transparency and credibility

Interpretability plays a central role in regulatory communications. Reviewers must be able to follow the logic from assumptions to conclusions, particularly for high-impact decisions. Clear, interpretable models facilitate efficient review, reduce ambiguity, and increase confidence that conclusions are robust and reproducible.

Interpretability is what allows a model to move from a technical artifact to regulatory evidence. Yet even interpretable models can lose credibility if the underlying process is not transparent. In a future post, we will examine the most common modeling mistakes that reduce reviewer trust and how to avoid them.

By Sandra Suarez-Sharp