Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds.

In this review an overview of the application of computational approaches is given. Specifically, the uses of Quantitative Structure-Activity Relationship (QSAR) methods for in silico identification of new families of compounds as novel tyrosinase inhibitors are revised. Assembling, validation of models through prediction series, and virtual screening of external data sets are also shown, to prove the accuracy of the QSAR models obtained with the TOMOCOMD-CARDD (TOpological MOlecular COMputational Design-Computer-Aided Rational Drug Design) software and Linear Discriminant Analysis (LDA) as statistical technique. Together with this, a database is collected for these QSAR studies, and could be considered a useful tool in future QSAR modeling of tyrosinase activity and for scientists that work in the field of this enzyme and its inhibitors. Finally, a translation to real world applications is shown by the use of QSAR models in the identification and posterior in-vitro evaluation of different families of compounds. Several different classes of compounds from various sources (natural and synthetic) were identified. Between them, we can find tetraketones, cycloartanes, ethylsteroids, lignans, dicoumarins and vanilloid derivatives. Finally, some considerations are discussed in order to improve the identification of novel drug-like compounds based on the use of QSAR-Ligand-Based Virtual Screening (LBVS).

Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays.

QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors employing Dragon descriptors and linear discriminant analysis (LDA) are presented here. A data set of 653 compounds, 245 with tyrosinase inhibitory activity and 408 having other clinical uses were used. The active data set was processed by k-means cluster analysis in order to design training and prediction series. Seven LDA-based QSAR models were obtained. The discriminant functions applied showed a globally good classification of 99.79% for the best model Class=-96.067+1.988 x 10(2)X0Av +9 1.907 BIC3 + 6.853 CIC1 in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. This external prediction set had an accuracy of 99.44%. After that, the developed models were used in ligand-based virtual screening of tyrosinase inhibitors from the literature and never considered in either training or predicting series. In this case, all screened chemicals were correctly classified by the LDA-based QSAR models. As a final point, these fitted models were used in the screening of new bipiperidine series as new tyrosinase inhibitors. These methods are an adequate alternative to the process of selection/identification of new bioactive compounds. The biosilico assays and in vitro results of inhibitory activity on mushroom tyrosinase showed good correspondence. It is important to stand out that compound BP4 (IC(50)=1.72 microM) showed higher activity in the inhibition against the enzyme than reference compound kojic acid (IC(50)=16.67 microM) and l-mimosine (IC(50)=3.68 microM). These results support the role of biosilico algorithm for the identification of new tyrosinase inhibitor compounds.

TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.

A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six using the non-stochastic total and local bond-based linear indices as well as the last six ones, the stochastic molecular descriptors. The best two discriminant models computed using the non-stochastic and stochastic molecular descriptors (Eqs. , respectively) had globally good classifications of 98.95% and 89.75% in the training set, with high Matthews correlation coefficients (C) of 0.98 and 0.78. The external prediction sets had accuracies of 98.89% and 89.44%, and (C) values of 0.98 and 0.78, for models 7 and 13, respectively. A virtual screening of compounds reported in the literature with such activity was carried out, to prove the ability of present models to search for tyrosinase inhibitors, not included in the training or test set. At the end, the fitted discriminant functions were used in the selection/identification of new ethylsteroids isolated from herbal plants, looking for tyrosinase inhibitory activity. A good behavior is shown between the theoretical and experimental results on mushroom tyrosinase enzyme. It might be highlighted that all the compounds showed values under 10microM and that ES2 (IC(50)=1.25microM) showed higher activity in the inhibition against the enzyme than reference compounds kojic acid (IC(50)=16.67microM) and l-mimosine (IC(50)=3.68microM). In addition, a comparison with other established methods was carried to prove the adequate discriminatory performance of the molecular descriptors used here. The present algorithm provided useful clues that can be used to speed up in the identification of new tyrosinase inhibitor compounds.

New tyrosinase inhibitors selected by atomic linear indices-based classification models.

In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behavior is shown between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds.

Studies of the anticancer potential of plants used in Bangladeshi folk medicine.

The present study evaluated the anticancer potential of 11 plants used in Bangladeshi folk medicine. The extracts were tested for cytotoxicity using the brine shrimp lethality assay, sea urchin eggs assay, hemolysis assay and MTT assay using tumor cell lines. The extract of Oroxylum indicum showed the highest toxicity on all tumor cell lines tested, with an IC(50) of 19.6 microg/ml for CEM, 14.2 microg/ml for HL-60, 17.2 microg/ml for B-16 and 32.5 microg/ml for HCT-8. On the sea urchin eggs, it inhibited the progression of cell cycle since the frist cleavage (IC(50)=13.5 microg/ml). The extract of Aegle marmelos exhibited toxicity on all used assays, but in a lower potency than Oroxylum indicum. In conclusion, among all tested extracts, only the extracts of Oroxylum indicum, Moringa oleifera and Aegles marmelos could be considered as potential sources of anticancer compounds. Further studies are necessary for chemical characterization of the active principles and more extensive biological evaluations.