Trends in kinase selectivity: insights for target class-focused library screening.

A kinome-wide selectivity screen of >20000 compounds with a rich representation of many structural classes has been completed. Analysis of the selectivity patterns for each class shows that a broad spectrum of structural scaffolds can achieve specificity for many kinase families. Kinase selectivity and potency are inversely correlated, a trend that is also found in a large set of kinase functional data. Although selective and nonselective compounds are mostly similar in their physicochemical characteristics, we identify specific features that are present more frequently in compounds that bind to many kinases. Our results support a scaffold-oriented approach for building compound collections to screen kinase targets.

Orthogonality of SFC versus HPLC for small molecule library separation.

Preparative HPLC and HPLC-MS are well established as the methods of choice for purification of pharmaceutical library compounds. Recent advances in supercritical fluid chromatography (SFC) have now made SFC a viable alternative to HPLC for this application. One of the potential arguments for using SFC in place of, or in addition to, HPLC is that it may offer different selectivity and thus has the potential for improved separation success rates. In this paper, we examine relative success rates for SFC and HPLC in obtaining adequate selectivity for successful separation. Our results suggest that use of SFC in addition to HPLC may result in a slight (1-2%) improvement in success rate compared to use of HPLC alone.

A simple strategy for mitigating the effect of data variability on the identification of active chemotypes from high-throughput screening data.

Among the several goals of a high-throughput screening campaign is the identification of as many active chemotypes as possible for further evaluation. Often, however, the number of concentration response curves (e.g., IC(50)s or K(i)s) that can be collected following a primary screen is limited by practical constraints such as protein supply, screening workload, and so forth. One possible approach to this dilemma is to cluster the hits from the primary screen and sample only a few compounds from each cluster. This introduces the question as to how many compounds must be selected from a cluster to ensure that an active compound is identified, if it exists at all. This article seeks to address this question using a Monte Carlo simulation in which the dependence of the success of sampling is directly linked to screening data variability. Furthermore, the authors demonstrate that the use of replicated compounds in the screening collection can easily assess this variability and provide a priori guidance to the screener and chemist as to the extent of sampling required to maximize chemotype identification during the triage process. The individual steps of the Monte Carlo simulation provide insight into the correspondence between the percentage inhibition and eventual IC(50) curves.

Measuring CAMD technique performance. 2. How "druglike" are drugs? Implications of Random test set selection exemplified using druglikeness classification models.

Research into the advancement of computer-aided molecular design (CAMD) has a tendency to focus on the discipline of algorithm development. Such efforts are often wrought to the detriment of the data set selection and analysis used in said algorithm validation. Here we highlight the potential problems this can cause in the context of druglikeness classification. More rigorous efforts are applied to the selection of decoy (nondruglike) molecules from the ACD. Comparisons are made between model performance using the standard technique of random test set creation with test sets derived from explicit ontological separation by drug class. The dangers of viewing druglike space as sufficiently coherent to permit simple classification are highlighted. In addition the issues inherent in applying unfiltered data and random test set selection to (Q)SAR models utilizing large and supposedly heterogeneous databases are discussed.

Are target-family-privileged substructures truly privileged?

One of the early and effective approaches to G-coupled protein receptor target family library design was the analysis of a set of ligands for frequently occurring chemical moieties or substructures. Various methods ranging from frameworks analysis to pharmacophores have been employed to find these so-called target-family-privileged substructures. Although the use of these substructures is common practice in combinatorial library design and has produced leads, the methods used for finding them rarely verified their selectivity for the particular target family from which they were derived. The frequency of occurrence among ligands associated with a target receptor family is not a sufficient criterion for those substructures to receive the label of target-family-privileged substructure. This study explores the question of selectivity of ClassPharmer generated fragments for a series of target families: GPCRs, nuclear hormone receptors, serine proteases, protein kinases, and ligand-gated ion channels. In addition, a GPCR focused library and a random set of 10k compounds are examined in terms of their target-family-privileged-substructure composition. The results challenge the combinatorial chemistry concept of target-family-privileged substructures and suggest that many of these fragments may simply be drug-like or attractive for various receptors in accordance with the original definition of privileged substructures.

Solid-phase synthesis of 5-substituted amino pyrazoles.

An efficient method for the solid-supported synthesis of 5-N-alkylamino and 5-N-arylamino pyrazoles is described. This method is general and mild and utilizes readily accessible resin-immobilized beta-ketoamides 2 as starting materials for the preparation of 1. Resin-immobilized beta-ketoamide, aryl-, or alkylhydazine and Lawesson's reagent are suspended in a mixture of THF/Py and heated at 50-55 degrees C to give a resin-bound 5-aminopyrazole, that is liberated from the solid support by treatment with TFA.

Measuring CAMD technique performance: a virtual screening case study in the design of validation experiments.

The dynamic nature and comparatively young age of computational chemistry is such that novel algorithms continue to be developed at a rapid pace. Such efforts are often wrought at the expense of extensive experimental validations of said techniques, preventing a deeper understanding of their potential utility and limitations. Here we address this issue for ligand-based virtual screening descriptors through design of validation experiments that better reflect the aims of real world application. Applying the newly defined chemotype enrichment approach, a variety of two- and three-dimensional (2D/3D) similarity descriptors have been compared extensively across data sets from four diverse target types. The inhibitors within said data sets contain molecules exhibiting a wide array of substructure functionality, size and flexibility, permitting descriptor comparison in myriad settings. Relative descriptor performance under these conditions is examined, including results obtained using more typical virtual screening validation experiments. Guidelines for optimal application of said descriptors are also discussed in the context of the results obtained, as is the potential utility of fingerprint filtering.

Biphenylsulfonamide endothelin receptor antagonists. 4. Discovery of N-[[2'-[[(4,5-dimethyl-3-isoxazolyl)amino]sulfonyl]-4-(2-oxazolyl)[1,1'-biphenyl]- 2-yl]methyl]-N,3,3-trimethylbutanamide (BMS-207940), a highly potent and orally active ET(A) selective antagonist.

We have previously disclosed the selective ET(A) receptor antagonist N-(3,4-dimethyl-5-isoxazolyl)-4'-(2-oxazolyl)[1,1'-biphenyl]-2-sulfonamide (1, BMS-193884) as a clinical development candidate. Additional SAR studies at the 2'-position of 1 led to the identification of several analogues with improved binding affinity as well as selectivity for the ET(A) receptor. Following the discovery that a 3-amino-isoxazole group displays significantly improved metabolic stability in comparison to its 5-regioisomer, the 3-amino-isoxazole group was combined with the optimal 2'-substituent leading to 16a (BMS-207940). Compound 16a is an extremely potent (ET(A) K(i) = 10 pM) and selective (80,000-fold for ET(A) vs ET(B)) antagonist. It is also 150-fold more potent and >6-fold more selective than 1. The bioavailability of 16a was 100% in rats and the systemic clearance and volume of distribution are higher than that of 1. In rats, intravenous 16a blocks big ET pressor responses with 30-fold greater potency than 1. After oral dosing at 3 micromol/kg, 16a displays enhanced duration relative to 1.

Genetic Algorithm guided Selection: variable selection and subset selection.

A novel Genetic Algorithm guided Selection method, GAS, has been described. The method utilizes a simple encoding scheme which can represent both compounds and variables used to construct a QSAR/QSPR model. A genetic algorithm is then utilized to simultaneously optimize the encoded variables that include both descriptors and compound subsets. The GAS method generates multiple models each applying to a subset of the compounds. Typically the subsets represent clusters with different chemotypes. Also a procedure based on molecular similarity is presented to determine which model should be applied to a given test set compound. The variable selection method implemented in GAS has been tested and compared using the Selwood data set (n = 31 compounds; v = 53 descriptors). The results showed that the method is comparable to other published methods. The subset selection method implemented in GAS has been first tested using an artificial data set (n = 100 points; v = 1 descriptor) to examine its ability to subset data points and second applied to analyze the XLOGP data set (n = 1831 compounds; v = 126 descriptors). The method is able to correctly identify artificial data points belonging to various subsets. The analysis of the XLOGP data set shows that the subset selection method can be useful in improving a QSAR/QSPR model when the variable selection method fails.

Testing non-additivity of biological activity in a combinatorial library.

Combinatorial chemistry offers new opportunities to generate and analyze QSAR data. Traditional QSAR attempts to correlate activity with structure. With combinatorial chemistry, it is possible to correlate activity directly with the reagents used in a combinatorial library. If one can determine which reagents lead to the compounds of highest activities, it may then be possible to predict active compounds in virtual libraries of 10(6) to 10(10) compounds. This would greatly facilitate library design and provide confidence that the best compounds are being considered for synthesis. An important question is whether the activity of a product molecule can be considered as a sum of its components. This is referred to as additivity between reagents. If there is non-additivity, it is necessary to identify and include the non-additive terms in the model in order to improve QSAR models. Presented here are methods for developing QSAR models relating compound activity to reagents and a method for detecting the second effects of side-chain non-additivity. If the reagents in a library are shown to be additive in their contribution to activity, simple QSAR based on additive models can be applied confidently to reagents. Testing non-additivity can also guide the synthesis of the library. If the contributions are shown to be additive then the strategy for library synthesis may be shifted to include many reagents of a given type but not to make all combinations. The result is more efficient use of resources. In the analysis of percent inhibition data of a combinatorial library an additive model using reagents as descriptors yields a R(2) of 0.43. Application of this method is probably appropriate for HTS single point data while methods employing topological or pharmacophore based descriptors would be necessary to adequately model IC50 data.