Mechanism-based classification of in vitro–in vivo correlation failures and model-informed strategies for drug product development

Abstract

Abstract In vitro–in vivo correlation (IVIVC) links in vitro dissolution to in vivo drug exposure, enabling the prediction of systemic pharmacokinetics and supporting formulation optimization, dissolution specification setting, and regulatory biowaivers for modified-release dosage forms. Its classical success relies on the assumption that the in vitro release profile remains the dominant surrogate of the in vivo input function, enabling point-to-point Level A relationships between the dissolved and absorbed fractions. This paradigm remains effective in dissolution-controlled systems, where formulation-dependent release rate differences reliably predict in vivo input rates. However, conventional IVIVC breaks down when systemic exposure is governed by drug-specific absorption properties, formulation-dependent post-dissolution phase behavior, physiology-sensitive luminal variability, or inherently complex multi-process release systems. This review proposes a conceptual framework that organizes these previously recognized challenges into four categories: absorption process–driven, formulation-driven, physiological variability–driven, and complex release–driven. The categories may coexist in a single product, and each is linked to modeling strategies reported to improve predictive performance. Physiologically based biopharmaceutics modeling, population pharmacokinetic-based IVIVC, and convolution-based approaches are examined in terms of how each explicitly separates dissolution from absorption. The three differ in how directly they represent the underlying biological and physicochemical processes. Particular emphasis is placed on how these strategies support model-informed dissolution specification setting, virtual bioequivalence assessment, and prospective formulation design within the verified domain of each model.

The authors' abstract, as published at the source. Archives of Pharmacal Research, 2026 · DOI ↗