ABSTRACT
The carbonic anhydrase isoform IX (hCAIX) is one of the main players in extracellular tumor pH regulation, and it is known to be overexpressed in breast cancer and other common tumors. hCA IX supports the growth and survival of tumor cells, and its expression is correlated with metastasis and resistance to therapies, making it an interesting biomarker for diagnosis and therapy. The aim of this work deals with the development of an MRI imaging probe able to target the extracellular non-catalytic proteoglycan-like (PG) domain of CAIX. For this purpose, a specific nanoprobe, LIP_PepC, was designed by conjugating a peptidic interactor of the PG domain on the surface of a liposome loaded with Gd-bearing contrast agents. A Mouse Mammary Adenocarcinoma Cell Line (TS/A) was chosen as an in vitro breast cancer model to test the developed probe. MRI results showed a high selectivity and sensitivity of the imaging probe toward hCAI-expressing TS/A cells. This approach appears highly promising for the in vivo translation of a diagnostic procedure based on the targeting of hCA IX enzyme expression.
ABSTRACT
A comparative quantitative structure-retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.
