Quantitative structure-activity relationship (QSAR) analysis of tumor-specificity of 1,2,3,4-tetrahydroisoquinoline...

We have previously reported on the relative cytotoxicity of a total of 38 1,2,3,4-tetrahydroisoquinoline derivatives against human oral squamous cell carcinoma cell lines and human normal oral cells, and the correlation between the cytotoxicity and 17 chemical descriptors. However, the correlation between the tumor-specificity of these compounds and the chemical descriptors has never been investigated so far. Using these previous data, we investigated various parameters for their applicability in predicting tumor specificity.

Original data of 50% cytotoxic concentration (CC(50)) values exceeding the maximum concentration in experimental conditions were corrected by the introduction of a harmonic mean, reducing the number of compounds analyzed to 30. The mean pCC(50) (=-log CC(50)) values for normal and tumor cells were defined as N and T, respectively. Tumor specificity was defined as the ratio of the difference of these values to their sum: (T-N)/(T+N). The chemical descriptors were obtained by quantum chemical calculations using semi-empirical (AM1, PM3, and PM6) and density functional theory methods. The relationship between the chemical descriptors and tumor specificity was analyzed by linear regression and artificial neural networks.

Out of 17 chemical descriptors, water-accessible surface area showed the highest correlation coefficient with tumor specificity, regardless of the method of calculation. Furthermore, neural network analysis demonstrated the importance of quantum chemical calculations in predicting the specificity of tetrahydroisoquinoline derivatives.

The present study suggests the applicability of quantum chemical descriptor in the estimation of tumor specificity of related compounds.