For decades, the pharmaceutical industry has pursued a reliable bridge between in vitro dissolution and in vivo performance. Despite meaningful progress, this remains one of the more persistent challenges in oral drug development—particularly for poorly soluble compounds and increasingly complex formulations.
We have long relied on in vitro–in vivo correlations (IVIVC) as a framework to connect these domains. In principle, the concept is sound: if dissolution can be measured in vitro, it should be possible to predict how a drug behaves in vivo. In practice, however, this translation is often less reliable than we would like.
The question, then, is not whether dissolution matters. It clearly does. The question is whether our current methods capture the reality of how drug products behave once they enter the human body.
Where Traditional IVIVC Falls Short
Classical IVIVC approaches typically rely on either direct input of in vitro dissolution data or the application of scaling factors for the in vivo dissolution time and fraction absorbed. These methods can work well under certain conditions, particularly when dissolution is not solubility limited and when the formulation excipients control drug release.
However, for many modern drug candidates—especially those with low solubility or complex formulation characteristics—these approaches frequently fail to provide consistent predictive performance. The reason is straightforward: in vitro dissolution experiments do not replicate in vivo conditions. Differences in pH, fluid composition, hydrodynamics, and volume can significantly alter dissolution behavior. Moreover, the presence of bile salt micelles and dynamic changes in the lumen physiology introduce additional layers of complexity that are not captured in standard in vitro dissolution tests.
The Z-factor is a mechanistic model that accounts for formulation effects in vitro and in vivo. Dissolution predictions using Z-factor are sensitive to dose, volume and solubility differences between in vitro and in vivo conditions. Z-factor, however, often lacks the granularity required to describe complex dissolution mechanisms or media. They are not inherently designed to handle formulation comprising multiple polymorphs, different micelles or non-first-order dissolution profiles. As a result, we are often left with models that fit data, but do not fully explain it.
A Shift Toward Mechanistic Understanding
Over time, it has become clear that empirical correlations alone were not sufficient. What is needed is a more mechanistic representation of dissolution—one that reflects not just the experimental conditions, but the physical reality of the drug product itself.
This shift aligns with the broader evolution toward model-informed drug development. Modeling is no longer used solely to interpret data after the fact; it is increasingly expected to guide decisions, inform formulation strategies, and support regulatory submissions.
To do so effectively, our models must be grounded in how drug products actually behave.
Rethinking the Drug Product: Introducing P-PSD
One of the key insights in this area is that the drug product is not simply a collection of particles defined by the drug substance (DS). The manufacturing process—blending, granulation, compression and the choice of the excipients—fundamentally alters the physical characteristics of the drug substance within the drug product.
Particles may fracture or agglomerate. Surface properties may change. Wettability may improve or deteriorate. The spatial distribution of the drug within the formulation can influence how much surface area is effectively available for dissolution. DS sizing methods cannot predict or capture these changes and this is where the concept of a product particle size distribution (P-PSD) becomes invaluable.
Rather than describing the DS in isolation, the P-PSD represents the effective surface area of the drug within the finished product—the surface that is actually available for dissolution under in vitro and physiological conditions. Importantly, the P-PSD is not measured directly. It is inferred by fitting observed in vitro dissolution data of the drug product itself, using mechanistic equations that account for the underlying dissolution processes. In doing so, it captures the combined effects of formulation, manufacturing, and physicochemical interactions in a way that is directly relevant to modeling.
From Dissolution Data to Mechanistic Representation
In practical terms, the P-PSD approach uses observed dissolution profiles from specific drug product batches as input. These data are then used to estimate a particle size distribution that reproduces the observed release behavior.
This process is not simply curve fitting. The underlying dissolution model incorporates key mechanisms, including:
- Surface-area–dependent dissolution represented by multiple bins of spherical particles in which the drug pass is distributed
- Drug binding to micelles in the dissolution medium (the affinity of the drug to micelles is measured independently)
- Potential drug degradation once it is solubilized (degradation rate constant can be measured separately from stock solutions)
- The influence of micelle size and number (the critical micelle concentration and drug affinity to micelles and micelle size are measured separately)
- The presence of multiple polymorphs, each with distinct solubility and affinity to micelles
- The potential precipitation from one polymorph to another (directly resulting from the dissolution equation)
The result is a P-PSD that reflects the physical and chemical realities of the formulation in a given dissolution medium. Once fitted to a given dataset, the P-PSD can be validated against additional dissolution conditions and then used as an input for physiologically based biopharmaceutics models (PBBM). This is where its true value emerges.
Why P-PSD Improves Predictive Confidence
By explicitly representing the surface area available for dissolution for each polymorph of the formulation, P-PSD provides a more realistic foundation for predicting in vivo behavior.
Several advantages follow:
- Improved mechanistic fidelity
The model reflects how the product actually dissolves, rather than relying on simplified assumptions. - Batch-specific insight
Variability between batches can be captured and evaluated, supporting more robust development strategies and enabling PBBM validation and waivers of clinical bioequivalence studies. - Reduced over-parameterization
The approach emphasizes parsimony, identifying the minimum level of complexity (bins of drug sizes) required to describe the system. - Enhanced integration with PBBM
Because P-PSD is grounded in physical reality, it translates more naturally into physiologically based models, where particles of drug are moved along the GI tract, with smaller highly soluble particles dissolving first, followed by larger lower solubility particles.
Ultimately, this leads to greater confidence—not only in the model itself, but in the decisions that depend on it.
Implications Across the Development Lifecycle
The impact of this approach extends across multiple stages of drug development.
In early development, P-PSD can support formulation screening by helping teams understand how processing decisions influence dissolution behavior. This comprises the choice of the right polymorph and size of the drug substance (through its impact on drug product dissolution).
Half way through development, it provides a more reliable basis for establishing in vitro–in vivo relationships, and allow PBBM to be developed. Because of its mechanistic nature, fitting can work both ways: in vivo PK profiles could be fitted with a P-PSD if in vivo dissolution is limiting absorption and in vitro dissolution of that drug product could be predicted, thereby increasing the likelihood and speed to develop a biopredictive dissolution method.
In later stages, it strengthens the scientific foundation of regulatory submissions by offering a mechanistic rationale for predicted in vivo performance, supporting formulation changes, waiving unnecessary human evaluation and helping with selected quality specifications of the drug product, such as the dissolution specification, polymorphic impurity level specification, or drug substance particle size specification.
Across all stages, the goal is the same: reduce uncertainty and improve the quality of decision-making.
From Correlation to Understanding
The evolution from empirical correlation to mechanistic understanding is not unique to dissolution modeling. It reflects a broader trend across pharmaceutical science.
As our tools become more sophisticated and our data more abundant, the expectation is no longer simply to integrate observations. It is to explain them—and to use that understanding to predict what will happen under new conditions.
In this context, approaches like P-PSD represent an important step forward.
They do not replace experimental data, nor do they eliminate uncertainty entirely. What they offer is a more faithful representation of the system we are trying to model.
And in drug development, fidelity matters.