In vitro study of interaction between quinine and Garcinia kola

Purpose: To investigate the interaction between quinine and Garcinia kola using an in vitro adsorption study. Methods: In vitro interaction between quinine and G. kola was conducted at 37 ± 0.1 °C. Adsorption of quinine (2.5 - 40 μg/ml) to 2.5 % w/v G. kola suspension was studied. Thereafter, quinine desorption process was investigated. The amount of quinine adsorbed and desorbed was quantified using HPLC. A Freundlich isotherm was constructed to describe the resulting data and percentage of quinine desorbed was determined from the desorption data. Results: An adsorption isotherm of the data gave a Freundlich constant (K) of 52.66 μg/g, with a slope of 0.69 indicating a high capacity and affinity of G. kola to adsorb quinine at a concentration smaller than 2.41 μg/g of G. kola. However the adsorptive capacity of G. kola for quinine at 37 ± 0.1 °C appears to be a saturable process as observed from the isotherm. Quinine desorption from G. kola peaked at 1 hour (37.51 %) and decreased to a constant amount (about 35 %) over the remaining sampling time. Conclusion: Quinine is adsorbed on G. kola in vitro. This suggests that concurrent administration of quinine and G. kola should be avoided, to prevent potential drug interaction and decreased drug bioavailability.

Pathway analysis for identification of potential biomarkers in severe cutaneous drug hypersensitivity reactions

Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.