In vitro- in vivo correlation's dissolution limits setting.

PURPOSE: In vitro in vivo correlation (IVIVC) is a biopharmaceutical tool recommended for use in formulation development. When validated, IVIVC can be used to set dissolution limits and, based on the dissolution limits, as a surrogate for an in vivo study. The purpose of this paper is to study the various methods used to fix dissolution limits. METHODS: Fixing dissolution limits is not a straightforward process; various approaches exist. The classical ±10% of dissolution limits was compared to the recommended ±10% of Cmax and AUC and to an innovative back calculation of the 90% CI. Based on simulated values the influence of the calculation method as well as of the variability of the results and pharmacokinetic processes was investigated. RESULTS: Depending upon the method, the results are different and their comparison leads to possible rules. It appears that the usage of a back calculation of a 90% CI is an accurate and advantageous method when intra-individual variability associated with the drug is low. Those findings are in accordance with the current practice of IVIVC, which is not recommended for highly variable drugs. CONCLUSIONS: The approach of using a 90% CI allows the intra-subject variability to be taken into account and fixes limits that ensure a greater chance to show acceptable BE, in case of reasonable intra-subject variability, leading to setting broader in vitro dissolution limits compared to classical solutions.

In vitro-in vivo correlations: tricks and traps.

In vitro-in vivo correlation (IVIVC) is a biopharmaceutical tool recommended to be used in development of formulation. When validated, it can speed up development of formulation, be used to fix dissolution limits and also as surrogate of in vivo study. However, as do all tools, it presents limitations and traps. The aim of the present paper is to investigate five common traps which could limit either the setting or use of IVIVC (1) using mean or individual values; (2) correction of absolute bioavailability; (3) correction of lag time and time scaling; (4) flip-flop model; and (5) predictability corrections.