Investigating the Effect of APAP Crystals on Tablet Behavior Manufactured by Direct Compression.

In this work, the effect of API's (Active Pharmaceutical Ingredient) shape and size on tablet characteristics is investigated for high API dose formulation manufactured by direct compression. Three different classes of APAP (acetaminophen) are selected, and tablets are produced in both single and batch processes. After performing and comparing comprehensive series of standard characterization tests including hardness, dissolution, disintegration, and friability on the tablets, the test results show the relation between the quality of APAP tablets and the shape and size of the crystals.

A novel co-processing method to manufacture an API for extended release formulation via formation of agglomerates of active ingredient and hydroxypropyl methylcellulose during crystallization.

A novel process for generating agglomerates of active pharmaceutical ingredient (API) and polymer by swelling the polymer in a water/organic mixture has been developed to address formulation issues resulting from a water sensitive, high drug load API with poor powder properties. Initially, the API is dissolved in water, following which hydroxypropyl methylcellulose (HPMC) is added, resulting in the imbibing of water, along with the dissolved API, into the HPMC matrix. The addition of acetone and isopropyl acetate (anti-solvents) then causes the API to crystallize inside and on the surface of HPMC agglomerates.

Investigating the applicability of inverse gas chromatography to binary powdered systems: an application of surface heterogeneity profiles to understanding preferential probe-surface interactions.

The aim of this study was to investigate the applicability of surface energy characterization tools such as inverse gas chromatography for the analysis of binary systems. Drug substance was coated with two grades of silicon dioxide and the surface energy characteristics determined using a surface energy analyser. The results demonstrated that the measured dispersive surface energy of such intermediate samples were as a consequence of probe interactions with both constituent components, however, the degree and order of interaction with each species was related to surface energy heterogeneity and surface availability.

Use of enthalpy and Gibbs free energy to evaluate the risk of amorphous formation.

The control of crystalline and amorphous phases is important during the development of a new drug candidate. Our approach begins with an understanding of the thermodynamics of these two phases. We have developed a quantitative yet practical work flow consisting of three steps towards the analysis of the risk of amorphous material formation.

Modeling of pan coating processes: Prediction of tablet content uniformity and determination of critical process parameters.

We developed an engineering model for predicting the active pharmaceutical ingredient (API) content uniformity (CU) for a drug product in which the active is coated onto a core. The model is based on a two-zone mechanistic description of the spray coating process in a perforated coating pan. The relative standard deviation (RSD) of the API CU of the coated tablets was found to be inversely proportional to the square root of the total number of cycles between the spray zone and drying zone that the tablets undergo.

Experimental and model-based approaches to studying mixing in coating pans.

The focus of this study was the determination of mixing patterns and rates inside a cylindrical coating pan. The research for this study was divided into two parts. The first part examined the mixing pattern and the movement of tablets inside of a coating pan experimentally.

Evaluation of risk and benefit in the implementation of near-infrared spectroscopy for monitoring of lubricant mixing.

On-line near-infrared spectroscopy (NIRS) was used to monitor lubricant blending to ensure the quality of the final dosage form. A quantitative multivariate NIR model was developed using different lubricant concentration levels. Real-time model predictions correlated well with the expected lubricant concentration during blending, which allowed determination of blend quality.