Ehab Hamed

Development of tamper deterrent formulations: state of the pharmaceutical industry.

Prescription drug abuse is a significant and growing health and socio-economical problem in the US and the world. According to the 2008 UN World Drug Report, the number of people who have consumed an illicit drug at least once in 2006/2007 reached 240 million, roughly 6% of the world population aged 15 to 64. In the last few years, pharmaceutical manufacturers started developing new formulations specifically designed to provide tamper deterrent features. The initial focus of these development activities was extended release opioids, owing to their dominant share of reported prescription drug abuse. Tamper deterrent formulations (TDF) for other drugs of abuse, including stimulants and sedatives are also in various stages of development. Three major challenges face the development of TDF: the increased sophistication of the tampering methods used by abusers, the ambiguity of the regulatory requirements for labeling and marketing and the exaggerated expectations of what these formulations can deliver. This review details the approaches used by pharmaceutical manufacturers to impart tamper deterrent features into their formulations; the in vitro and in vivo tests that have been proposed or used to assess the performance of TDF; and the current regulatory landscape.

An exact model for predicting tablet and blend content uniformity based on the theory of fluctuations in mixtures

Content uniformity (CU) of tablets is a critical property that needs to be well controlled in pharmaceutical products. Methods that predict the CU accurately can greatly help in reducing the development efforts. This article presents a statistical mechanical framework for predicting CU based on first principles at the molecular level. The tablet is modeled as an open system that can be treated as a grand canonical ensemble to calculate fluctuations in the number of granules and thus the CU. Exact analytical solutions to hard sphere mixture systems are applied to derive an expression for the CU and elucidate the different factors that impact CU. The model was tested against literature data and a large set of tablet formulations specifically made and analyzed for CU using a model active pharmaceutical ingredient. The formulations covered the effect of granule size, percentage loading, and tablet weight on the CU. The model is able to predict the mean experimental coefficient of variation (CV) with good success and captures all the elements that impact the CU. The predictions of the model serve as a theoretical lower limit for the mean CV (for infinite batches or tablets) that can be expected during manufacturing assuming the best processing conditions.