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Today, a growing number of subcutaneously administered depot formulations enable continuous delivery of poorly soluble compounds over a longer time period. The modified liberation is considered to be a rate-limiting step in drug absorption and thus impacts therapeutic efficacy and product safety. In the present approach, a mechanism-based pharmacokinetic model of the commercial microparticle formulation depo-subQ provera 104™ (Sauter mean diameter of 5.08 ± 1.63 µm) was established. The model was verified using human pharmacokinetic data from three different clinical trials. Further, the effects of drug release, injection site and patient population on the pharmacokinetic profile were investigated. For this purpose, the drug release was assessed using the novel dispersion releaser technology, whereby a biorelevant medium reflecting major characteristics of the subcutaneous tissue (including ion background, buffer capacity and protein concentration) was used. The established model provided an effective prediction of the key pharmacokinetic parameters, including Cmax, Tmax and AUCall. Only in presence of 55% of fetal bovine serum (using a novel simulated subcutaneous interstitial fluid), the release assay was capable to discriminate between microparticles before and after storage.

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Liver volume is a critical scaling factor for predicting drug clearance in physiologically based pharmacokinetic modelling and for both donor/recipient graft size estimation in liver transplantation. The accurate and precise estimation of liver volume is therefore essential. The objective here was to extend an existing meta-analysis using a non-linear mixed effects modelling approach for the estimation of liver volume to other race groups and paediatric and geriatric populations. Interrogation of the PubMed® database was undertaken using a text string query to ensure as objective a retrieval of liver volume data for the modelling exercise as possible. Missing body size parameters were estimated using simulations from the Simcyp Simulator V13R1 for an age and ethnically appropriate population. Non-linear mixed effect modelling was undertaken in Phoenix 1.3 (Certara) utilizing backward deletion and forward inclusion of covariates from fully parameterized models. Existing liver volume models based on body surface area (BSA) and body weight and height were implemented for comparison. The extension of a structural model using a BSA equation and incorporating the Japanese race and age as covariates and exponents on LV0 (θBaseline ) and body surface area (θBSA ), respectively, delivered a comparatively low objective function value. Bootstrapping of the original dataset revealed that the confidence intervals (2.5-97.5%) for the fitted (theta) parameter estimates were bounded by the bootstrapped estimates of the same. In conclusion, extension and re-parameterization of the existing Johnson model adequately describes changes in liver volume using the body surface area in all investigated populations. Copyright © 2017 John Wiley & Sons, Ltd.

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X-ray-based alignments of bioactive compounds are commonly used to correlate structural changes with changes in potencies, ultimately leading to three-dimensional quantitative structure-activity relationships such as CoMFA or CoMSIA models that can provide further guidance for the design of new compounds. We have analyzed data sets where the alignment of the compounds is entirely based on experimentally derived ligand poses from X-ray-crystallography. We developed CoMFA and CoMSIA models from these X-ray-determined receptor-bound conformations and compared the results with models generated from ligand-centric Template CoMFA, finding that the fluctuations in the positions and conformations of compounds dominate X-ray-based alignments can yield poorer predictions than those from the self-consistent template CoMFA alignments. Also, when there exist multiple different binding modes, structural interpretation in terms of binding site constraints can often be simpler with template-based alignments than with X-ray-based alignments.

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Template CoMFA, a novel alignment methodology for training or test set structures in 3D-QSAR, is introduced. Its two most significant advantages are its complete automation and its ability to derive a single combined model from multiple structural series affecting a biological target. Its only two inputs are one or more "template" structures having 3D coordinates that share some Cartesian space, as may result from X-ray crystallography or pharmacophoric hypothesis, and one or more connectivity-only SAR tables associated with a common target. Template CoMFA also overcomes the major disadvantages of both existing 3D-QSAR alignment methodologies, specifically the tedium and subjectivity of familiar ad hoc approaches, and the awkwardness, occasional physicochemical heresies, and structural scope limitations of the purely topomer approach. The template CoMFA algorithms are described, and two of its application classes are presented. The first class, general models of binding to factor Xa and P38 map kinase, uses crystallographic structures as templates, with the encouraging result that the statistical qualities of each of these two combined models are equivalent to those of their constituent individual series models. The second, 15 data sets originally collected for validation of topomer CoMFA, with arbitrary structures as templates, confirms that the modeling power of template CoMFA resembles that of its predecessors.

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New heterocyclic derivatives of 9-azajulolidine have been synthesized and characterized with respect to their nucleophilicity and Lewis basicity. The Lewis basicity of these bases as quantified through their theoretically calculated methyl-cation affinities correlate well with the experimentally measured reaction rates for addition to benzhydryl cations. All newly synthesized pyridines show exceptional catalytic activities in benchmark acylation reactions, which correlate only poorly with Lewis basicity or nucleophilicity parameters. A combination of Lewis basicity with charge and geometric parameters in the framework of a three-component quantitative structure-activity relationship (QSAR) model is, however, highly predictive.

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Inhibitors of hypoxia-inducible factor 1 (HIF-1) represent promising anticancer therapeutics. We have identified a series of potent toluidinesulfonamide HIF-1 inhibitors. However, the series was threatened by a potential liability to inhibit CYP2C9 which could cause dangerous drug-drug interactions when being coadministered with other drugs. We used structure-activity data from the PubChem database to develop a topomer CoMFA model that guided the design of novel sulfonamides with high selectivity for HIF-1 over CYP2C9 inhibition.

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Modeling off-target effects is one major goal of chemical biology, particularly in its applications to drug discovery. Here, we describe a new approach that allows the extraction of structure-activity relationships from large chemogenomic spaces starting from a single chemical structure. Several public source databases, offering a vast amount of data on structure and activity for a large number of different targets, have been investigated for their usefulness in automated structure-activity relationships (SAR) extraction. SAR tables were constructed by assembling similar structures around each query structure that have an activity record for a particular target. Quantitative series enrichment analysis (QSEA) was applied to these SAR tables to identify trends and to transform these trends into topomer CoMFA models. Overall more than 1700 SAR tables with topomer CoMFA models have been obtained from the ChEMBL, PubChem, and ChemBank databases. These models were able to highlight the structural trends associated with various off-target effects of marketed drugs, including cases where other structural similarity metrics would not have detected an off-target effect. These results indicate the usefulness of the QSEA approach, particularly whenever applicable with public databases, in providing a new means, beyond a simple similarity between ligand structures, to capture SAR trends and thereby contribute to success in drug discovery.

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A novel procedure is proposed for 3D-QSAR analysis. The composition of 16 published QSAR datasets has been examined using Quantitative Series Enrichment Analysis (QSEA). The procedure is based on topomer technologies. A heatmap display in combination with topomer CoMFA and a novel series trajectory analysis revealed critical information for the assembly of structures into meaningful series. Global and local centroid structures can be determined from a similarity distance matrix and build the origins for stepwise model building by increasing the similarity radius around the centroid nucleus. The results indicate that the new procedure allows determination of whether compounds belong to an emerging structure-activity relationship and which compounds can be predicted within reliable limits.

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Based primarily on further studies of a collection of eleven publications reporting fifteen successful 3D-QSAR relations, several phenomena are preliminarily described. The RMS error of 133 ligand binding energy predictions based on these successful 3D-QSARs is 0.75 kcal/mole, which compares favorably to the prediction accuracies of approaches that include the receptor. A similar result is obtained when topomer alignments are substituted for those published, with seemingly profound implications for the future of 3D-QSAR. The "alignment-averaged" molecular properties, log P and molar refractivity, have very little correlative power for these data sets, either alone or in combination with the 3D-QSAR field descriptors. The q (2 )metric for the number of PLS components necessarily tends to discard any unique or unconfirmed SAR information. Large drops in q (2) are thus to be expected whenever such unique information is first encountered. Predictive r (2) values from an exploratory new "series trajectory" analysis of these 3D-QSAR though highly variable do not differ much from their q (2) values, a phenomenon that seems to encourage prediction even when there are so few structures underlying a 3D-QSAR so that almost all information is unique.

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Crystal structures taken from the Cambridge Structural Database were used to build a ring scaffold database containing 19 050 3D structures, with each such scaffold then being used to generate a centroid connecting path (CCP) representation. The CCP is a novel object that connects ring centroids, ring linker atoms, and other important points on the connection path between ring centroids. Unsupervised searching in the scaffold and CCP data sets was carried out using the atom-based LAMDA and RigFit search methods and the field-based similarity search method. The performance of these methods was tested with three different ring scaffold queries. These searches demonstrated that unsupervised 3D scaffold searching methods can find not only the types of ring systems that might be retrieved in carefully defined pharmacophore searches (supervised approach) but also additional, structurally diverse ring systems that could form the starting point for lead discovery programs or other scaffold-hopping applications. Not only are the methods effective but some are sufficiently rapid to permit scaffold searching in large chemical databases on a routine basis.

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Two extensive studies quantifying the ability of topomer shape similarity to forecast a variety of biological similarities are described. In a prospective trial of "lead hopping", using topomer similarity for virtual screening and queries from the patent literature, biological assays of 308 selected compounds (representing 0.03% of those available, per assay type) yielded 11 successful "lead hops" in the 13 assays attempted. The hit rate averaged over all assays was 39% ("activity"defined as inhibition > or =20% at 10 microM), significantly greater than an unexpectedly high negative control hit rate of 15%. The average "Tanimoto 2D fingerprint similarity" between query and "lead hop" structures (0.36) was little more than the Tanimoto similarity between random drug-like structures. Topomer shape and Tanimoto 2D fingerprint similarities were also compared retrospectively, in their tendencies to concentrate together potential and actual drugs reported to belong to the same "activity class", for twenty classes. Among the most similar 3% of structures (corresponding to "> or =0.85 Tanimoto" for these structures), an average of 62% of the topomer similar selection possessed a near neighbor belonging to the same activity class, roughly a one-third superiority over the "Tanimoto > or = 0.85" selection containing 48% actives in avoiding false positives. Conversely, the least similar 75% of structures contained 0.3% actives for topomer similarity vs 1.0% actives for Tanimoto 2D fingerprint similarity, a 3-fold superiority for topomers in avoiding false negatives.

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Efficient and general procedures have been developed for the solution-phase preparation of substituted morpholine derivatives, and a library has been produced around generic structure 1. This library was designed with proprietary modeling software for use as a general screening library. The 30 R1 reagents were phenols, and the 275 R2 reagents were taken from five different reagent classes, giving a variety of product classes in the final library of 8250 potential products. All of the library members were generated from a common intermediate, mesylate (5), which was synthesized efficiently, in bulk, in three steps from N-benzylethanolamine (2). High-throughput chemistry using robotics was carried out to produce the 7907 library members, which were individually characterized by reversed-phase LC/MS analysis.

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