Evolution of a physiological pH 6.8 bicarbonate buffer system: application to the dissolution testing of enteric coated products.

The use of compendial pH 6.8 phosphate buffer to assess dissolution of enteric coated products gives rise to poor in vitro-in vivo correlations because of the inadequacy of the buffer to resemble small intestinal fluids. A more representative and physiological medium, pH 6.8 bicarbonate buffer, was developed to evaluate the dissolution behaviour of enteric coatings. The bicarbonate system was evolved from pH7.4 Hanks balanced salt solution to produce a pH 6.8 bicarbonate buffer (modified Hanks buffer, mHanks), which resembles the ionic composition and buffer capacity of intestinal milieu. Prednisolone tablets were coated with a range of enteric polymers: hypromellose phthalate (HP-50 and HP-55), cellulose acetate phthalate (CAP), hypromellose acetate succinate (HPMCAS-LF and HPMCAS-MF), methacrylic acid copolymers (EUDRAGIT® L100-55, EUDRAGIT® L30D-55 and EUDRAGIT® L100) and polyvinyl acetate phthalate (PVAP). Dissolution of coated tablets was carried out using USP-II apparatus in 0.1M HCl for 2h followed by pH 6.8 phosphate buffer or pH 6.8 mHanks bicarbonate buffer. In pH 6.8 phosphate buffer, the various enteric polymer coated products displayed rapid and comparable dissolution profiles. In pH 6.8 mHanks buffer, drug release was delayed and marked differences were observed between the various coated tablets, which is comparable to the delayed disintegration times reported in the literature for enteric coated products in the human small intestine. In summary, the use of pH 6.8 physiological bicarbonate buffer (mHanks) provides more realistic and discriminative in vitro release assessment of enteric coated formulations compared to compendial phosphate buffer

A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure

Conditional value-at-risk, Sample average approximation, Smoothing method, Stochastic optimization,

Robust approaches to pension fund asset liability management under uncertainty

This entry considers the problem of a typical pension fund that collects premiums from sponsors or employees and is liable for fixed payments to its customers after retirement. The fund manager’s goal is to determine an investment strategy so that the fund can cover its liabilities while minimizing contributions from its sponsors and maximizing the value of its assets. We develop robust optimization and scenario-based stochastic programming approaches for optimal asset-liability management, taking into consideration the uncertainty in asset returns and future liabilities. Our focus is on computational tractability and ease of implementation under conditions typically encountered in practice, such as asymmetries in the distributions of asset returns. Computational results from tests with real and generated data are presented to illustrate the performance of these models