Simulations Plus

Mechanistically simulate in vitro disintegration and dissolution across a range of pharmaceutical dosage forms and experimental conditions. Explore how API properties, excipients, formulation composition, particle size, and experimental parameters can influence dissolution behavior.

Before committing additional time and materials to experimental testing, use simulated scenarios to investigate formulation strategies, support dissolution method development, and evaluate potential sources of variability.

Incorporate drug product batch dissolution data directly into PBBM workflows to better represent the formulation administered to patients.

Account for the combined effects of excipients, process parameters, and other formulation factors reflected in measured dissolution data, then use that information to explore how changes in formulation or product performance may influence predicted in vivo exposure.

Develop mechanistic or traditional relationships between in vitro release and in vivo release or absolute bioavailability.

Use mechanistic deconvolution to estimate in vivo release along the gastrointestinal tract or apply established deconvolution approaches to characterize the relationship between dissolution and systemic exposure. Evaluate correlation functions and prediction statistics to assess the ability of the model to describe observed data.

Apply established in vitro-in vivo relationships and PBBM models to explore formulations with different dissolution profiles or dose strengths.

Simulate resulting plasma concentration-time profiles and evaluate predicted Cmax and AUC to investigate whether changes in drug product performance may meaningfully affect systemic exposure.

Use integrated dissolution and PBBM simulations to investigate questions arising throughout formulation and drug product development.

Explore formulation strategies, dissolution specifics, lot-to-lot variability, and alternative dissolution conditions while connecting those changes to predicted in vivo performance. Apply the resulting mechanistic understanding to support model-informed formulation development, bioequivalence assessments, and regulatory applications.