1,3,4-Oxadiazole: An Emerging Scaffold to Inhibit the Thymidine Phosphorylase as an Anticancer Agent.

Thymidine phosphorylase (TP), also referred to as "platelet-derived endothelial cell growth factor" is crucial to the pyrimidine salvage pathway. TP reversibly transforms thymidine into thymine and 2-deoxy-D-ribose-1-phosphate (dRib-1-P), which further degraded to 2-Deoxy-D-ribose (2DDR), which has both angiogenic and chemotactic activity. In several types of human cancer such as breast and colorectal malignancies, TP is abundantly expressed in response to biological disturbances like hypoxia, acidosis, chemotherapy, and radiation therapy. TP overexpression is highly associated with angiogenic factors such as vascular endothelial growth factor (VEGF), interleukins (ILs), matrix metalloproteases (MMPs), etc., which accelerate tumorigenesis, invasion, metastasis, immune response evasion, and resistant to apoptosis. Hence, TP is recognized as a key target for the development of new anticancer drugs. Heterocycles are the primary structural element of most chemotherapeutics. Even 75% of nitrogen-containing heterocyclic compounds are contributing to the pharmaceutical world. To create the bioactive molecule, medicinal chemists are concentrating on nitrogen-containing heterocyclic compounds such as pyrrole, pyrrolidine, pyridine, imidazole, pyrimidines, pyrazole, indole, quinoline, oxadiazole, benzimidazole, etc. The Oxadiazole motif stands out among all of them due to its enormous significance in medicinal chemistry. The main thrust area of this review is to explore the synthesis, SAR, and the significant role of 1,3,4-oxadiazole derivatives as a TP inhibitor for their chemotherapeutic effects.

On two novel parameters for validation of predictive QSAR models.

Validation is a crucial aspect of quantitative structure-activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q(2) for internal validation and predictive R(2) for external validation) may be supplemented with two novel parameters r(m)(2) and R(p)(2) for a stricter test of validation. The parameter r(m)(2)((overall)) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter R(p)(2) penalizes model R(2) for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of r(m)(2) parameter, r(m)(2)((LOO)) and r(m)(2)((test)), penalize a model more strictly than Q(2) and R(2)(pred) respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q(2) and R(2)(pred)) but fail to achieve the required values for the novel parameters r(m)(2) and R(p)(2). Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques.

Twenty-eight structurally diverse cytochrome 3A4 (CYP3A4) inhibitors have been subjected to quantitative structure-activity relationship (QSAR) studies. The analyses were performed with electronic, spatial, topological, and thermodynamic descriptors calculated using Cerius 2 version 10 software. The statistical tools used were linear [multiple linear regression with factor analysis as preprocessing step (FA-MLR), stepwise MLR, partial least squares (PLS), genetic function algorithm (GFA), genetic PLS (G/PLS)] and non-linear methods [artificial neural network (ANN)]. All the five linear modeling methods indicate the importance of n-octanol/water partition coefficient (logP) along with different topological and electronic parameters. The best model obtained from the training set (stepwise regression) based on highest external predictive R(2) value and lowest RMSEP value also showed good internal predictive power. Other models like FA-MLR, PLS, GFA and G/PLS are also of statistically significant internal and external validation characteristics. The best model [according to r(m)(2) for the test set, as defined by P.P. Roy, K. Roy, QSAR Comb. Sci. 27 (2008) 302-313] obtained from ANN showed a good r(2) value (determination coefficient between observed and predicted values) for the test set compounds, which was superior to those of other statistical models except the stepwise regression derived model. However, based upon the r(m)(2) value (test set), which penalizes a model for large differences between observed and predicted values, the stepwise MLR model was found to be inferior to other methods except PLS. Considering r(m)(2) value for the whole set, the G/PLS derived model appears to be the best predictive model for this data set. For choosing the best predictive model from among comparable models, r(m)(2) for the whole set calculated based on leave-one-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion.