WITHDRAWN--a resource for withdrawn and discontinued drugs.

Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a multitude of non-safety problems including lack of efficacy, manufacturing, regulatory or business issues. During the last century, the majority of drugs voluntarily withdrawn from the market or prohibited by regulatory agencies was reported to be related to adverse drug reactions. Understanding the underlying mechanisms of toxicity is of utmost importance for current and future drug discovery. Here, we present WITHDRAWN, a resource for withdrawn and discontinued drugs publicly accessible at http://cheminfo.charite.de/withdrawn. Today, the database comprises 578 withdrawn or discontinued drugs, their structures, important physico-chemical properties, protein targets and relevant signaling pathways. A special focus of the database lies on the drugs withdrawn due to adverse reactions and toxic effects. For approximately one half of the drugs in the database, safety issues were identified as the main reason for withdrawal. Withdrawal reasons were extracted from the literature and manually classified into toxicity types representing adverse effects on different organs. A special feature of the database is the presence of multiple search options which will allow systematic analyses of withdrawn drugs and their mechanisms of toxicity.

A Maximum-Likelihood Approach to Force-Field Calibration.

A new approach to the calibration of the force fields is proposed, in which the force-field parameters are obtained by maximum-likelihood fitting of the calculated conformational ensembles to the experimental ensembles of training system(s). The maximum-likelihood function is composed of logarithms of the Boltzmann probabilities of the experimental conformations, calculated with the current energy function. Because the theoretical distribution is given in the form of the simulated conformations only, the contributions from all of the simulated conformations, with Gaussian weights in the distances from a given experimental conformation, are added to give the contribution to the target function from this conformation. In contrast to earlier methods for force-field calibration, the approach does not suffer from the arbitrariness of dividing the decoy set into native-like and non-native structures; however, if such a division is made instead of using Gaussian weights, application of the maximum-likelihood method results in the well-known energy-gap maximization. The computational procedure consists of cycles of decoy generation and maximum-likelihood-function optimization, which are iterated until convergence is reached. The method was tested with Gaussian distributions and then applied to the physics-based coarse-grained UNRES force field for proteins. The NMR structures of the tryptophan cage, a small α-helical protein, determined at three temperatures (T = 280, 305, and 313 K) by Halabis et al. ( J. Phys. Chem. B 2012 , 116 , 6898 - 6907 ), were used. Multiplexed replica-exchange molecular dynamics was used to generate the decoys. The iterative procedure exhibited steady convergence. Three variants of optimization were tried: optimization of the energy-term weights alone and use of the experimental ensemble of the folded protein only at T = 280 K (run 1); optimization of the energy-term weights and use of experimental ensembles at all three temperatures (run 2); and optimization of the energy-term weights and the coefficients of the torsional and multibody energy terms and use of experimental ensembles at all three temperatures (run 3). The force fields were subsequently tested with a set of 14 α-helical and two α + ß proteins. Optimization run 1 resulted in better agreement with the experimental ensemble at T = 280 K compared with optimization run 2 and in comparable performance on the test set but poorer agreement of the calculated folding temperature with the experimental folding temperature. Optimization run 3 resulted in the best fit of the calculated ensembles to the experimental ones for the tryptophan cage but in much poorer performance on the training set, suggesting that use of a small α-helical protein for extensive force-field calibration resulted in overfitting of the data for this protein at the expense of transferability. The optimized force field resulting from run 2 was found to fold 13 of the 14 tested α-helical proteins and one small α + ß protein with the correct topologies; the average structures of 10 of them were predicted with accuracies of about 5 Å C(α) root-mean-square deviation or better. Test simulations with an additional set of 12 α-helical proteins demonstrated that this force field performed better on α-helical proteins than the previous parametrizations of UNRES. The proposed approach is applicable to any problem of maximum-likelihood parameter estimation when the contributions to the maximum-likelihood function cannot be evaluated at the experimental points and the dimension of the configurational space is too high to construct histograms of the experimental distributions.

Strategies for the discovery of biased GPCR ligands.

G-protein-coupled receptors (GPCRs) represent important drug targets with complex pharmacological characteristics. Biased signaling represents one important dimension, describing ligand-dependent shifts of naturally imprinted signaling profiles. Because biased GPCR modulators provide potential therapeutic benefits including higher efficiencies and reduced adverse effects, the identification of such ligands as drug candidates is highly desirable. This review aims to provide an overview of the challenges and strategies in the discovery of biased ligands. We show different approaches for biased ligand discovery in the example of G-protein-biased opioid analgesics and discuss possibilities to design biased ligands by targeting extracellular receptor regions.

Arginase Structure and Inhibition: Catalytic Site Plasticity Reveals New Modulation Possibilities.

Metalloenzyme arginase is a therapeutically relevant target associated with tumor growth. To fight cancer immunosuppression, arginase activity can be modulated by small chemical inhibitors binding to its catalytic center. To better understand molecular mechanisms of arginase inhibition, a careful computer-aided mechanistic structural investigation of this enzyme was conducted. Using molecular dynamics (MD) simulations in the microsecond range, key regions of the protein active site were identified and their flexibility was evaluated and compared. A cavity opening phenomenon was observed, involving three loops directly interacting with all known ligands, while metal coordinating regions remained motionless. A novel dynamic 3D pharmacophore analysis method termed dynophores has been developed that allows for the construction of a single 3D-model comprising all ligand-enzyme interactions occurring throughout a complete MD trajectory. This new technique for the in silico study of intermolecular interactions allows for loop flexibility analysis coupled with movements and conformational changes of bound ligands. Presented MD studies highlight the plasticity of the size of the arginase active site, leading to the hypothesis that larger ligands can enter the cavity of arginase. Experimental testing of a targeted fragment library substituted by different aliphatic groups validates this hypothesis, paving the way for the design of arginase inhibitors with novel binding patterns.