PubMed · 12664537
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M F M Engels. 2003. Creating knowledge from high-throughput screening data.. https://doi.org/10.1007/978-3-662-05314-0_7
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Extended release formulations of isosorbide mononitrate (IMN), based on osmotic technology, were developed. Target release profile was selected and different variables were optimized to achieve the same. Formulation variables like type (PVP, PEG-4000, and HPMC) and level of pore former (0-55%, w/w of polymer), percent weight gain were found to affect the drug release from the developed formulations. Drug release was inversely proportional to the membrane weight but directly related to the initial level of pore former in the membrane. Burst strength of the exhausted shells was inversely proportional to the level of pore former, but directly affected by the membrane weight. Satisfactory burst strength (more than 320 g) was obtained when PVP was used as pore former (up to 55%, w/w of polymer) at the membrane weight of 7.5% and more. The release from the developed formulations was independent of pH and agitational intensity, but dependent on the osmotic pressure of the release media. Results of SEM studies showed the formation of pores in the membrane from where the drug release occurred. The formulations were found to be stable after 3 months of accelerated stability studies. Prediction of steady-state levels showed the plasma concentrations of IMN to be within the desired range.
Oil-in-water emulsions varying in surfactant concentration and manufacturing process were prepared. About 10 experiments were performed to characterize them. The goal of this research was to find out which tests should systematically be carried out to assess efficiently the stability and the properties of an emulsified preparation. Thus, formulation design requires at least the measurement of the droplet size, the determination of the zeta potential, a TurbiScan analysis, the investigation of the stability under centrifugation and freeze/thaw cycles. If the emulsion contains an active substance, stability under storage at 4 degrees C and microscopic analysis are relevant. Quality control should be improved by measurements of viscosity and pH.
A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, stability, as well as safety must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in an RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. The purpose of this review is to describe the basic concept of the multi-objective simultaneous optimization technique, in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. Superior function of the ANN approach was demonstrated by the optimization for typical numerical examples.