Systems biology approach to study permeability of paracetamol and its solid dispersion

Physiological changes that take place at cellular level are usually reflective of their level of gene expression. Different formulation excipients have an impact on physiological behavior of the exposed cells and in turn affect transporter genes, enterocyte-mediated metabolism and toxicity biomarkers. The aim of this study was to prepare solid dispersion of paracetamol and evaluate genetic changes that occur in Caco-2 cell lines during the permeability of paracetamol alone and paracetamol solid dispersion formulations. Paracetamol-PEG 8000 solid dispersion was prepared by melt fusion method and the formulation was characterised using differential scanning calorimetry (DSC), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Formulation of solid dispersion resulted in the conversion of crystalline drug into an amorphous form. Permeability studies showed that paracetamol absorption was higher from the solid dispersion formulation. DNA microarrays analysis was carried out in order to investigate the involvement of any efflux/uptake transporters in paracetamol or its solid dispersion permeability. Neither transporter carriers nor efflux proteins were found to be involved in the absorption of paracetamol or its PEG solid dispersion. Gene expression analysis established that paracetamol toxicity was potentially reduced upon formulation into solid dispersion when ATP binding cassette (ABC) and solute carrier transporter (SLC) genes were analyzed.

A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. © 2008 Springer Science + Business Media LLC.