Gemcitabine Integrated Nano-Prodrug Carrier System

Peptide nanomaterials have received a great deal of interest in drug-delivery applications due to their biodegradability, biocompatibility, suitability for large-scale synthesis, high drug-loading capacities, targeting ability, and ordered structural organization. The covalent conjugation of drugs to peptide backbones results in prolonged circulation time and improved stability of drugs. Therapeutic efficacy of gemcitabine, which is used for breast cancer treatment, is severely compromised due to its rapid plasma degradation. Its hydrophilic nature poses a challenge for both its efficient encapsulation into nanocarrier systems and its sustained release property. Here, we designed a new peptide prodrug molecule for the anticancer drug gemcitabine, which was covalently conjugated to the C-terminal of 9-fluorenylmethoxy carbonyl (Fmoc)-protected glycine. The prodrug was further integrated into peptide nanocarrier system through noncovalent interactions. A pair of oppositely charged amyloid-inspired peptides (Fmoc-AIPs) were exploited as components of the drug-carrier system and self-assembled into one-dimensional nanofibers at physiological conditions. The gemcitabine integrated nanoprodrug carrier system exhibited slow release and reduced the cellular viability of 4T1 breast cancer cell line in a time- and concentration-dependent manner. © 2017 American Chemical Society

Using the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification

<p>Abstract</p> <p>Background</p> <p>Microarray-based tumor classification is characterized by a very large number of features (genes) and small number of samples. In such cases, statistical techniques cannot determine which genes are correlated to each tumor type. A popular solution is the use of a subset of pre-specified genes. However, molecular variations are generally correlated to a large number of genes. A gene that is not correlated to some disease may, by combination with other genes, express itself.</p> <p>Results</p> <p>In this paper, we propose a new classiification strategy that can reduce the effect of over-fitting without the need to pre-select a small subset of genes. Our solution works by taking advantage of the information embedded in the testing samples. We note that a well-defined classification algorithm works best when the data is properly labeled. Hence, our classification algorithm will discriminate all samples best when the testing sample is assumed to belong to the correct class. We compare our solution with several well-known alternatives for tumor classification on a variety of publicly available data-sets. Our approach consistently leads to better classification results.</p> <p>Conclusion</p> <p>Studies indicate that thousands of samples may be required to extract useful statistical information from microarray data. Herein, it is shown that this problem can be circumvented by using the information embedded in the testing samples.</p