Application of Hansch’s Model to Capsaicinoids and Capsinoids: A Study Using the Quantitative Structure−Activity Relationship. A Novel Method for the Synthesis of Capsinoids

We describe a synthetic approach for two families of compounds, the capsaicinoids and capsinoids, as part of a study of the quantitative relationship between structure and activity

Diazoxide attenuates autoimmune encephalomyelitis and modulates lymphocyte proliferation and dendritic cell functionality

Activation of mitochondrial ATP-sensitive potassium (KATP) channels is postulated as an effective mechanism to confer cardio and neuroprotection, especially in situations associated to oxidative stress. Pharmacological activation of these channels inhibits glia-mediated neuroinflammation. In this way, diazoxide, an old-known mitochondrial KATP channel opener, has been proposed as an effective and safe treatment for different neurodegenerative diseases, demonstrating efficacy in different animal models, including the experimental autoimmune encephalomyelitis (EAE), an animal model for Multiple Sclerosis. Although neuroprotection and modulation of glial reactivity could alone explain the positive effects of diazoxide administration in EAE mice, little is known of its effects on the immune system and the autoimmune reaction that triggers the EAE pathology. The aim of the present work was to study the effects of diazoxide in autoimmune key processes related with EAE, such as antigen presentation and lymphocyte activation and proliferation. Results show that, although diazoxide treatment inhibited in vitro and ex-vivo lymphocyte proliferation from whole splenocytes it had no effect in isolated CD4(+) T cells. In any case, treatment had no impact in lymphocyte activation. Diazoxide can also slightly decrease CD83, CD80, CD86 and major histocompatibility complex class II expression in cultured dendritic cells, demonstrating a possible role in modulating antigen presentation. Taken together, our results indicate that diazoxide treatment attenuates autoimmune encephalomyelitis pathology without immunosuppressive effect

The role of ligand efficiency metrics in drug discovery

The judicious application of ligand or binding efficiencies, which quantify the molecular properties required to gain binding affinity for a drug target, is gaining traction in the selection and optimisation of fragments, hits, and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimised ligand efficiency values for their target. Optimising ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the developability of drug candidates

Large-scale evaluation of log P predictors: Local corrections may compensate insufficient accuracy and need of experimentally testing every other compound.

A large variety of log P calculation methods failed to produce sufficient accuracy in log P prediction for two in-house datasets of more than 96000 compounds contrary to their significantly better performances on public datasets. The minimum Root Mean Squared Error (RMSE) of 1.02 and 0.65 were calculated for the Pfizer and Nycomed datasets, respectively, in the 'out-of-box' implementation. Importantly, the use of local corrections (LC) implemented in the ALOGPS program based on experimental in-house log P data significantly reduced the RMSE to 0.59 and 0.48 for the Pfizer and Nycomed datasets, respectively, instantly without retraining the model. Moreover, more than 60% of molecules predicted with the highest confidence in each set had a mean absolute error (MAE) less than 0.33 log units that is only ca. 10% higher than the estimated variation in experimental log P measurements for the Pfizer dataset. Therefore, following this retrospective analysis, we suggest that the use of the predicted log P values with high confidence may eliminate the need of experimentally testing every other compound. This strategy could reduce the cost of measurements for pharmaceutical companies by a factor of 2, increase the confidence in prediction at the analog design stage of drug discovery programs, and could be extended to other ADMET properties

Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds.

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 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 log P predictions were elucidated and discussed. 2008 Willey-Liss, Inc. and the American Pharmacists Association J Pharm Sci

Accurate in silico log P predictions: One can't embrace the unembraceable.

Prediction accuracy of in silico methods for physicochemical and ADMET properties of drugs is an actual matter of controversial discussions. With a particular concern on log P prediction methods, we discuss here, how understanding the limitations of methods, their applicability domains and their prediction accuracies, as well as the use of local models can help to establish accurate and meaningful in silico predictions

Protein-protein interactions : an overview

No abstract