Promising Aedes aegypti repellent chemotypes identified through integrated QSAR, virtual screening, synthesis, and bioassay.

Molecular field topology analysis, scaffold hopping, and molecular docking were used as complementary computational tools for the design of repellents for Aedes aegypti, the insect vector for yellow fever, chikungunya, and dengue fever. A large number of analogues were evaluated by virtual screening with Glide molecular docking software. This produced several dozen hits that were either synthesized or procured from commercial sources. Analysis of these compounds by a repellent bioassay resulted in a few highly active chemicals (in terms of minimum effective dosage) as viable candidates for further hit-to-lead and lead optimization effort.

Aminopyridinecarboxamide-based inhaled IKK-2 inhibitors for asthma and COPD: Structure-activity relationship.

Installation of sites for metabolism in the lead compound PHA-767408 was the key focus of the IKK-2 inhaled program. This paper reports our efforts to identify a novel series of aminopyridinecarboxamide-based IKK-2 inhibitors, which display low nanomolar potency against IKK-2 with long duration of action (DOA), and metabolically labile to phase I and/or phase II metabolizing enzymes with potential capability for multiple routes of clearance. Several compounds have demonstrated their potential usefulness in the treatment of asthma and chronic obstructive pulmonary disease (COPD).

Structure-based drug design enables conversion of a DFG-in binding CSF-1R kinase inhibitor to a DFG-out binding mode.

The work described herein demonstrates the utility of structure-based drug design (SBDD) in shifting the binding mode of an HTS hit from a DFG-in to a DFG-out binding mode resulting in a class of novel potent CSF-1R kinase inhibitors suitable for lead development.

Expression, purification and functional characterization of IkappaB kinase-2 (IKK-2) mutants.

NF-kappaB signaling plays a pivotal role in a variety of pathological conditions. Because of its central role in the overall NF-kappaB regulation, IKK-2 is a viable target for drug discovery. In order to enable structure-based design of IKK-2 inhibitors, we carried out a rational generation of IKK-2 mutants based on induced-fit docking of a selective IKK-2 inhibitor, PHA-408, into the homology model of IKK-2. One mutant we have characterized is a catalytically inactive form of IKK-2, D145A IKK-2, wherein the catalytic aspartic acid, D145 was replaced with alanine. Unlike the WT enzyme, D145A IKK-2 is devoid of kinase activity despite its ability to bind ATP with high affinity and is not phosphorylated at the T loop. In addition, this mutant binds a diverse collection of inhibitors with comparable binding affinities to WT IKK-2. Another interesting mutant we have characterized is F26A IKK-2 (F26 is an aromatic residue located at the very tip of the Gly-rich loop). Pre-incubation of F26A IKK-2 with PHA-408 revealed the role of F26 in the time-dependent binding of this inhibitor. Thus, functional characterization of these mutants provides the first evidence showing the role of a Gly-rich loop residue of a kinase in binding kinetics. These two mutants along with others that we have identified could be used to validate homology models and probe the interactions of IKK-2 with a variety of inhibitors.

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.

Benzothiophene inhibitors of MK2. Part 2: improvements in kinase selectivity and cell potency.

Optimization of kinase selectivity for a set of benzothiophene MK2 inhibitors provided analogs with potencies of less than 500 nM in a cell based assay. The selectivity of the inhibitors can be rationalized by examination of X-ray crystal structures of inhibitors bound to MK2.

Benzothiophene inhibitors of MK2. Part 1: structure-activity relationships, assessments of selectivity and cellular potency.

Identification of potent benzothiophene inhibitors of mitogen activated protein kinase-activated protein kinase 2 (MK2), structure-activity relationship (SAR) studies, selectivity assessments against CDK2, cellular potency and mechanism of action are presented. Crystallographic data provide a rationale for the observed MK2 potency as well as selectivity over CDK2 for this class of inhibitors.

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 log P 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: log P = 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.

Pyrrolopyridine inhibitors of mitogen-activated protein kinase-activated protein kinase 2 (MK-2).

A new class of potent kinase inhibitors selective for mitogen-activated protein kinase-activated protein kinase 2 (MAPKAP-K2 or MK-2) for the treatment of rheumatoid arthritis has been prepared and evaluated. These inhibitors have IC50 values as low as 10 nM against the target and have good selectivity profiles against a number of kinases including CDK2, ERK, JNK, and p38. These MK-2 inhibitors have been shown to suppress TNFalpha production in U397 cells and to be efficacious in an acute inflammation model. The structure-activity relationships of this series, the selectivity for MK-2 and their activity in both in vitro and in vivo models are discussed. The observed selectivity is discussed with the aid of an MK-2/inhibitor crystal structure.

Structure-activity relationships for interaction with multidrug resistance protein 2 (ABCC2/MRP2): the role of torsion angle for a series of biphenyl-substituted heterocycles.

Multidrug resistance protein 2 (ABCC2/MRP2) is an ATP-binding cassette transporter involved in the absorption, distribution, and excretion of drugs and xenobiotics. Identifying compounds that are ABCC2/MRP2 substrates and/or inhibitors and understanding their structure-activity relationships (SARs) are important considerations in the selection and optimization of drug candidates. In the present study, the interactions between ABCC2/MRP2 and a series of biphenyl-substituted heterocycles were evaluated using Caco-2 cells and human ABCC2/MRP2 gene-transfected Madin-Darby canine kidney cells. It was observed that ABCC2/MRP2 transport and/or inhibition profile, both in nature and in magnitude, depends strongly on the substitution patterns of the biphenyl system. In particular, different ortho-substitutions cause various degrees of twisting between the two-phenyl rings, resulting in changing interactions between the ligands and ABCC2/MRP2. The compounds with small ortho functions (hydrogen, fluorine, and oxygen) and, thus, the ones displaying the smallest torsion angles of biphenyl (37-45 degrees) are neither substrates nor inhibitors of human ABCC2/MRP2. The transporter interactions increase as the steric bulkiness of the ortho-substitutions increase. When the tested compounds are 2-methyl substituted biphenyls, they exhibit moderate torsion angles (54-65 degrees) and behave as ABCC2/MRP2 substrates as well as mild inhibitors [10-40% compared with 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethyl-sulfanyl)methylsulfanyl] propionic acid (MK571)]. For the 2,2'-dimethyl substituted biphenyls, the torsions are enhanced (78-87 degrees) and so is the inhibition of ABCC2/MRP2. This class of compounds behaves as strong inhibitors of ABCC2/MRP2. These results can be used to define the three-dimensional structural requirements of ABCC2/MRP2 interaction with their substrates and inhibitors, as well as to provide SAR guidance to support drug discovery.

Can we estimate the accuracy of ADME-Tox predictions?

There have recently been developments in the methods used to access the accuracy of the prediction and applicability domain of absorption, distribution, metabolism, excretion and toxicity models, and also in the methods used to predict the physicochemical properties of compounds in the early stages of drug development. The methods are classified into two main groups: those based on the analysis of similarity of molecules, and those based on the analysis of calculated properties. An analysis of octanol-water distribution coefficients is used to exemplify the consistency of estimated and calculated accuracy of the ALOGPS program (http://www.vcclab.org) to predict in-house and publicly available datasets.

Structure-based design and synthesis of pyrazinones containing novel P1 'side pocket' moieties as inhibitors of TF/VIIa.

We describe the structure-based design, synthesis, and enzymatic activity of a series of substituted pyrazinones as inhibitors of the TF/VIIa complex. These inhibitors contain substituents meta to the P(1) amidine designed to explore additional interactions with the VIIa residues in the so-called 'S(1) side pocket'. A crystal structure of the designed inhibitors demonstrates the ability of the P(1) side pocket moiety to engage Lys192 and main chain of Gly216 via hydrogen bond interactions, thus, providing additional possibility for chemical modification to improve selectivity and/or physical properties of inhibitors.

Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds.

Evaluation of the ALOGPS, ACD Labs LogD, and PALLAS PrologD suites to calculate the log D distribution coefficient resulted in high root-mean-squared error (RMSE) of 1.0-1.5 log for two in-house Pfizer's log D data sets of 17,861 and 640 compounds. Inaccuracy in log P prediction was the limiting factor for the overall log D estimation by these algorithms. The self-learning feature of the ALOGPS (LIBRARY mode) remarkably improved the accuracy in log D prediction, and an rmse of 0.64-0.65 was calculated for both data sets.