A promising strategy in cancer therapy aims to promote apoptosis in cancer cells. Targeting inhibitor of apoptosis proteins (IAPs) with small-molecule inhibitors has attracted increasing interest in ...triggering cancer cell death. It is considered to have great potential for cancer drug discovery because IAPs block apoptosis at the core of the apoptotic machinery and are aberrantly expressed in various tumors. This review focuses on the current development of small-molecule IAP antagonists for cancer therapy.
Ligand-based virtual screening approaches were applied to search for new chemotype KCOs activating Kir6.2/SUR1 KATP channels. A total of 65 208 commercially available compounds, extracted from the ...ZINC archive, served as database for screening. In a first step, pharmacokinetic filtering via VolSurf reduced the initial database to 1913 compounds. Afterward, six molecules were selected as templates for similarity searches: similarity scores, obtained toward these templates, were calculated with the GRIND, FLAP, and TOPP approaches, which differently encode structural information into potential pharmacophores. In this way, we obtained 32 hit candidates, 16 via GRIND and eight each via FLAP and TOPP. For biological testing of the hit candidates, their effects on membrane potentials in HEK 293 cells expressing Kir6.2/SUR1 were studied. GRIND, FLAP, and TOPP all yielded hits, but no method top-ranked all the actives. Thus, parallel application of different approaches probably improves hit detection.
We first review the state-of-the-art in development of logP prediction approaches falling in two major categories: substructure-based and property-based methods. Then, we compare the predictive power ...of representative methods for one public (N=266) and two in house datasets from Nycomed (N=882) and Pfizer (N=95809). A total of 30 and 18 methods were tested for public and industrial datasets, respectively. Accuracy of models declined with the number of nonhydrogen atoms. The Arithmetic Average Model (AAM), which predicts the same value (the arithmetic mean) for all compounds, was used as a baseline model for comparison. Methods with Root Mean Squared Error (RMSE) greater than RMSE produced by the AAM were considered as unacceptable. The majority of analyzed methods produced reasonable results for the public dataset but only seven methods were successful on the both in house datasets. We proposed a simple equation based on the number of carbon atoms, NC, and the number of hetero atoms, NHET: logP=1.46(±0.02)+0.11(±0.001) NC−0.11(±0.001) NHET. This equation outperformed a large number of programs benchmarked in this study. Factors influencing the accuracy of logP predictions were elucidated and discussed.
We collected 1173 hERG patch clamp (PC) data (IC50) from the literature to derive twelve classification models for hERG inhibition, covering a large variety of chemical descriptors and classification ...algorithms. Models were generated using 545 molecules and validated through 258 external molecules tested in PC experiments. We also evaluated the suitability of the best models to predict the activity of 26 proprietary compounds tested in radioligand binding displacement (RBD). Results proved the necessity to use multiple validation sets for a true estimation of model accuracy and demonstrated that using various descriptors and algorithms improves the performance of ligand-based models. Intriguingly, one of the most accurate models uncovered an unexpected link between extent of metabolism and hERG liability. This hypothesis was fairly reinforced by using the Biopharmaceutics Drug Disposition Classification System (BDDCS) that recognized 94% of the hERG inhibitors as extensively metabolized in vivo. Data mining suggested that high Torsades de Pointes (TdP) risk results from an interplay of hERG inhibition, extent of metabolism, active transport, and possibly solubility. Overall, these new findings might improve both the decision making skills of pharmaceutical scientists to mitigate hERG liability during the drug discovery process and the TdP risk assessment during drug development.
