Investigation of design space for freeze-drying: use of modeling for primary drying segment of a freeze-drying cycle.

In this work, we explore the idea of using mathematical models to build design space for the primary drying portion of freeze-drying process. We start by defining design space for freeze-drying, followed by defining critical quality attributes and critical process parameters. Then using mathematical model, we build an insilico design space. Input parameters to the model (heat transfer coefficient and mass transfer resistance) were obtained from separate experimental runs. Two lyophilization runs are conducted to verify the model predictions. This confirmation of the model predictions with experimental results added to the confidence in the insilico design space. This simple step-by-step approach allowed us to minimize the number of experimental runs (preliminary runs to calculate heat transfer coefficient and mass transfer resistance plus two additional experimental runs to verify model predictions) required to define the design space. The established design space can then be used to understand the influence of critical process parameters on the critical quality attributes for all future cycles.

Leveraging elevated temperature and particle size reduction to extract API from various tablet formulations.

Several sample preparation techniques were evaluated for extracting active pharmaceutical ingredient (API) from immediate release (IR) and controlled release (CR) tablet formulations. These techniques utilized either elevated temperature [e.g., accelerated solvent extraction (ASE) and microwave assisted extraction (MAE)] or particle size reduction [e.g., ball mill and homogenizer/Tablet Processing Workstation II (TPWII)]. Results were compared for equivalence to those obtained with the existing standard method for each formulation. For the CR formulations, sample preparation times were significantly reduced when using these techniques compared to the standard method. Advantages and limitations associated with each technique are discussed.

Statistical assessment of dissolution and drug release profile similarity using a model-dependent approach.

A general multivariate procedure for assessing the similarity of dissolution and drug release profiles was developed. A mathematical model is fit to the data, and Hotelling's T(2) test is used to calculate the joint confidence region around the vector of differences between least-squares estimates of the parameters in the model. The method of Lagrange multipliers is used to determine if this confidence region is enclosed within a predetermined similarity region, and profile similarity is claimed if this is the case. The first-order, Gompertz, logistic, second-order, and Weibull models were fit to the in vitro extended-release profile of pseudoephedrine HCl from an asymmetric membrane (AM) film-coated osmotic tablet. The first-order model was selected because of its simplicity and because it was the best-fitting model according to a modified form of Akaike's Information Criterion. A nonlinear response surface model was also developed so that the formulator could calculate how much of the AM film coat should be applied in order to obtain the desired drug release profile. The usefulness of this model-dependent procedure was further demonstrated during an analytical method transfer exercise, where it was used to compare the drug release profiles obtained by two independent laboratories; additional research is required, however, before the appropriate acceptance criteria for demonstrating profile similarity can be recommended.

Probability of passing dissolution acceptance criteria for an immediate release tablet.

During development of solid dosage products, a pharmaceutical manufacturer is typically required to propose dissolution acceptance criteria unless the product falls into Biopharmaceutics Classification System (BCS) class I, in which case a disintegration test may be used. At the time of filing the new drug application (NDA) or common technical document (CTD), the manufacturer has already met with regulatory agencies to discuss and refine dissolution strategy. The dissolution acceptance criteria are based on stability and batch history data and are often arrived at by considering the percentage of batches that pass United States Pharmacopeia (USP) criteria at Stage 1 (S(1)), when in fact, the product is deemed unacceptable only when a batch fails USP criteria at Stage 3 (S(3)) [H. Saranadasa, Disso. Technol. 7 (2000) 6-7, 18 [1]]. Calculating the probability of passing (or failing) dissolution criteria at S(1), S(2), or S(3) can assist a manufacturer in determining appropriate acceptance criteria. This article discusses a general statistical method that was developed to assess the probability of passing the multistage USP test for dissolution and how it was applied to an immediate release tablet formulation. In this case, acceptance criteria were set and the analysis was conducted to assess the probabilities of passing or failing based on this acceptance criterion. Whether the acceptance criteria were relevant to the product was also considered. This mathematical approach uses a Monte Carlo simulation and considers a range of values for standard deviation and mean of historical data.