The metabolic profiles of pterin compounds as potential biomarkers of bladder cancer-Integration of analytical-based approach with biostatistical methodology.

Cancer disease is the second leading cause of death across the world. The analysis of potential biomarkers of cancer can be useful in cancer screening or cancer diagnosis, and may provide valuable information on the disease risk and progression. Pterin compounds have been studied as candidates of potential biomarkers as their elevated levels have been reported in various cancer diseases. The objective of the study was to compare the profiles of six pterin compounds in urine of 35 healthy subjects and 46 patients diagnosed of bladder cancer with the use of HPLC coupled with fluorimetric detection. The results of the chromatographic analysis together with biostatistical-based approach showed, that the concentrations of pterin compounds in bladder cancer patients were higher as compared to healthy individuals, and statistically significant differences between patients and controls were reported for xanthopterin and isoxanthopterin. Moreover, gender-specific analysis revealed, that the concentrations of pterins in the group of women reached higher values in comparison to men. For metabolites juxtaposed in pairs, namely xanthopterin and isoxanthopterin as well as for neopterin and biopterin, we found significant positive correlations in the group of both, patients and healthy individuals. We therefore conclude, that chromatographic analysis with simultaneous extensive biostatistical-based interpretation of the metabolite profiles may provide deeper understanding of the relationships between pterin metabolites. The results do not prejudge the possibility of using pterin compounds in the diagnosis of bladder tumors. However the results may have an impact on the study of bladder cancer biomarkers.

QSRR and QSAR Studies of Antitumor Drugs in View of their Biological Activity Prediction.

BACKGROUND: The QSRRs and QSARs are relatively new approaches to relate internal chemical structure and particular biological activity. This methodology is based on theory that mechanisms which took place into chromatography column are similar to those that occur in a living organism at the molecular level, for example when compounds penetrate into cells. OBJECTIVE: In this paper, we aim to describe different cytostatic activities of selected anticancer drugs as QSRR and QSAR models, and prove usefulness of connected QSRR and QSAR methodology in different types of studies. METHOD: Chromatographic experiments using gradient RP-HPLC method and different C18 stationary phases were performed. As a result we obtained retention parameter log kw. Moreover, to calculate descriptors, which characterize lipophilicity of analyzed antitumor drugs, DryLab program was utilized. Molecular modeling studies were performed by using HyperChem program. Dragon software was used to calculate structural descriptors, and then selected descriptors were used to build QSRR and QSAR models. Obtained data were analyzed by multiple regression analysis (MLR). RESULTS: Experimental log kw and predicted log kw from QSRR models developed, were further used in QSAR analysis. The goodness of fit was in the range of R2= 0.75 - 0.95, and the predictive performance of the models was Q2 = 0.6 - 0.81. CONCLUSION: Both QSRR and QSAR strategies presented in this paper, allowed predicting HPLC retention parameters and cytotoxic activities of anticancer medicines without the necessity to carry out time-consuming and expensive experimental tests.

The application of connected QSRR and QSAR strategies to predict the physicochemical interaction of acridinone derivatives with DNA.

Acridinone derivatives as imidazoacridinones and triazoloacridinones are the new potent antitumor agents characterized by different mechanisms of action related to their ability to interact with DNA. The analysis undertaken in this study involves searching of QSAR (Quantitative Structure-Activity Relationship) and QSRR (Quantitative Structure- Retention Relationship) models, which would allow to predict the biological activity of acridinones expressed as the ability to stabilize the secondary structure of DNA (ΔT), based on their structural parameters and chromatographic retention data. For this purpose, 20 acridinone derivatives were subjected to chromatographic analyses and molecular modeling, followed by statistical analyses using multiple linear regression method (MLR). As a novelty aspect, except for RP-HPLC approach, hydrophilic interaction chromatography (HILIC) columns were tested. As a result of performed analysis, appropriate QSAR and QSRR models were obtained, and each model was analyzed in terms of prediction of acridinones' ability to interact with DNA. Derived QSAR and QSRR models were characterized as one, with good prediction performance. Conclusively, the proposed connected QSAR and QSRR strategies allow to predict in silico the ability of acridinones to interact with DNA without the necessity of performing any biological experiments under in vitro and in vivo conditions.